From 5189a2f61fc41acd7d481a21d47417875d28d12a Mon Sep 17 00:00:00 2001 From: root Date: Sun, 25 Oct 2026 08:27:39 +0800 Subject: [PATCH 01/31] feat(tools): add baseline summary, report fill, run compare and rank probe --- tests/test_batch_summary.py | 191 ++++++++++++++++ tests/test_compare_runs.py | 83 +++++++ tools/batch_summary.py | 440 ++++++++++++++++++++++++++++++++++++ tools/compare_runs.py | 192 ++++++++++++++++ tools/fill_baseline.py | 53 +++++ tools/rank_probe.py | 169 ++++++++++++++ 6 files changed, 1128 insertions(+) create mode 100644 tests/test_batch_summary.py create mode 100644 tests/test_compare_runs.py create mode 100644 tools/batch_summary.py create mode 100644 tools/compare_runs.py create mode 100644 tools/fill_baseline.py create mode 100644 tools/rank_probe.py diff --git a/tests/test_batch_summary.py b/tests/test_batch_summary.py new file mode 100644 index 0000000..977bcbb --- /dev/null +++ b/tests/test_batch_summary.py @@ -0,0 +1,191 @@ +"""批量汇总器：多 run 聚合、重复题去重、环境失败分类、Markdown 渲染。""" + +from __future__ import annotations + +import importlib.util +import json +from pathlib import Path + +_TOOL = Path(__file__).resolve().parents[1] / "tools" / "batch_summary.py" +_spec = importlib.util.spec_from_file_location("batch_summary", _TOOL) +batch_summary = importlib.util.module_from_spec(_spec) +assert _spec.loader is not None +_spec.loader.exec_module(batch_summary) + + +def _make_run(root: Path, name: str, episodes: dict[str, dict]) -> Path: + run_dir = root / name + for task, payload in episodes.items(): + episode_dir = run_dir / "episodes" / task + episode_dir.mkdir(parents=True, exist_ok=True) + (episode_dir / "episode.json").write_text( + json.dumps(payload, ensure_ascii=False), encoding="utf-8" + ) + (episode_dir / "timing.json").write_text( + json.dumps({"wall_seconds": payload.get("_wall", 60)}), encoding="utf-8" + ) + (episode_dir / "llm_calls.jsonl").write_text( + json.dumps({ + "role": "plan_step", + "response": {"usage": {"prompt_tokens": 100, "completion_tokens": 10}}, + }) + "\n", + encoding="utf-8", + ) + return run_dir + + +def test_collect_rows_merges_duplicates_preferring_scored(tmp_path): + """同一题在补跑片与被杀的片都有时，取已出分的那条。""" + _make_run(tmp_path, "batch-s1", { + "004": {"task_id": "004", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "TimeoutError", + "error": "VM failed to become ready within timeout period", "steps": 0}, + }) + _make_run(tmp_path, "batch-retry", { + "004": {"task_id": "004", "status": "scored", "score": 0.5, + "stop_reason": "done", "steps": 7}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch") + assert len(rows) == 1 + assert rows[0]["status"] == "scored" + assert rows[0]["score"] == 0.5 + + +def test_summarize_classifies_env_versus_model_failures(tmp_path): + _make_run(tmp_path, "batch", { + "001": {"task_id": "001", "status": "scored", "score": 1.0, "stop_reason": "done", "steps": 5}, + "002": {"task_id": "002", "status": "scored", "score": 0.0, + "stop_reason": "no_action_limit", "steps": 3}, + "003": {"task_id": "003", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "TimeoutError", + "error": "VM failed to become ready within timeout period", "steps": 0}, + "004": {"task_id": "004", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "SlowModelSchemaError", + "error": "slow model response is missing required fields", "steps": 0}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch") + stats = batch_summary.summarize(rows) + assert stats["total"] == 4 + assert stats["scored"] == 2 + assert [row["task"] for row in stats["env_failed"]] == ["003"] + assert [row["task"] for row in stats["model_failed"]] == ["004"] + assert stats["full_hits"] == 1 + assert stats["zeros"] == 1 + assert stats["average_scored"] == 0.5 + assert stats["calls"] == 4 + assert stats["prompt_tokens"] == 400 + + +def test_render_markdown_lists_rerun_tasks(tmp_path): + _make_run(tmp_path, "batch", { + "003": {"task_id": "003", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "TimeoutError", + "error": "VM failed to become ready within timeout period", "steps": 0}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch") + report = batch_summary.render_markdown(rows, batch_summary.summarize(rows), "T") + assert "需补跑" in report + assert "TASKS=003" in report + assert "003" in report + + +def test_collect_rows_ignores_unrelated_prefix(tmp_path): + _make_run(tmp_path, "other-batch", { + "001": {"task_id": "001", "status": "scored", "score": 1.0, "stop_reason": "done", "steps": 1}, + }) + assert batch_summary.collect_rows(tmp_path, "batch") == [] + + +def test_classify_task_covers_every_failure_mode(): + """每种结束方式都要落到一个标签，方便按题定位 harness 模块。""" + cases = { + "env_failure": {"status": "failed_unscored", "error": "VM failed to become ready", + "exception": "TimeoutError"}, + "model_schema": {"status": "failed_unscored", "exception": "SlowModelSchemaError", + "error": "slow model response is missing required fields"}, + "infeasible": {"status": "failed_unscored", "error": "", "exception": None, + "stop": "BudgetWise is a personal-finance website, not a wireless carrier"}, + "no_action": {"status": "scored", "score": 0.0, "stop": "no_action_limit"}, + "wall_limit": {"status": "scored", "score": 0.0, "stop": "wall_time_limit"}, + "max_steps": {"status": "scored", "score": 0.0, "stop": "max_steps"}, + "done_full": {"status": "scored", "score": 1.0, "stop": "done"}, + "done_partial": {"status": "scored", "score": 0.4, "stop": "done"}, + "done_zero": {"status": "scored", "score": 0.0, "stop": "done"}, + } + for expected, payload in cases.items(): + assert batch_summary.classify_task(payload) == expected, expected + # 未出分但停止原因是 max_steps：仍归到 max_steps，不落到 done_zero + assert batch_summary.classify_task( + {"status": "failed_unscored", "score": None, "stop": "max_steps"}) == "max_steps" + + +def test_render_markdown_groups_tasks_by_failure_mode(tmp_path): + _make_run(tmp_path, "batch", { + "001": {"task_id": "001", "status": "scored", "score": 1.0, "stop_reason": "done", "steps": 3}, + "002": {"task_id": "002", "status": "scored", "score": 0.0, + "stop_reason": "no_action_limit", "steps": 2}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch") + report = batch_summary.render_markdown(rows, batch_summary.summarize(rows), "T") + assert "失败模式分布" in report + assert "done_full" in report and "no_action" in report + + +def test_duplicate_task_prefers_newer_run_on_tie(tmp_path): + """补跑片与原片同题同分时取更新的那次（旧错误不应掩盖新结果）。""" + _make_run(tmp_path, "batch-20261024-000000-s1", { + "075": {"task_id": "075", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "ValueError", + "error": "No API key found for provider 'openai'", "steps": 0}, + }) + _make_run(tmp_path, "batch-20261025-023704-s1", { + "075": {"task_id": "075", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "BadRequestError", + "error": "media exceeds size limit: max 10485760 bytes", "steps": 0}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch") + assert len(rows) == 1 + # 补跑批次时间戳更晚 → 应被选中（旧的缺凭据错误已被取代） + assert "media exceeds size limit" in rows[0]["error"] + assert batch_summary.run_timestamp("batch-20261025-023704-s1") > \ + batch_summary.run_timestamp("batch-20261024-000000-s1") + + +def test_steps_fall_back_to_events_when_episode_json_says_zero(tmp_path): + """异常路径下 episode.json 的 steps=0，用事件流回补真实步数。""" + run = _make_run(tmp_path, "batch", { + "071": {"task_id": "071", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "HTTPError", + "error": "HTTP Error 422", "steps": 0}, + }) + events = run / "episodes" / "071" / "events.jsonl" + events.write_text( + "\n".join(json.dumps({"payload": {"step": i}}) for i in range(40)) + "\n", + encoding="utf-8") + rows = batch_summary.collect_rows(tmp_path, "batch") + assert rows[0]["steps"] == 39 + + +def test_classify_task_detects_judge_infra_failure(): + """judge 媒体上限属于评估基础设施（重跑无望），不是模型能力问题。""" + row = {"status": "failed_unscored", "score": None, "stop": "exception", + "exception": "BadRequestError", + "error": "Error code: 400 - media exceeds size limit: max 10485760 bytes"} + assert batch_summary.classify_task(row) == "judge_infra" + assert batch_summary.is_judge_infra_failure(row) + + +def test_collect_rows_accepts_multiple_prefixes(tmp_path): + """补跑片前缀常与主批次不同：逗号分隔多前缀要能一起收（取最新批次那次）。""" + _make_run(tmp_path, "batch-a", { + "001": {"task_id": "001", "status": "failed_unscored", "score": None, + "stop_reason": "exception", "exception_type": "HTTPError", + "error": "HTTP Error 429", "steps": 0}, + }) + _make_run(tmp_path, "batch-b-retry", { + "001": {"task_id": "001", "status": "scored", "score": 1.0, + "stop_reason": "done", "steps": 31}, + }) + rows = batch_summary.collect_rows(tmp_path, "batch-a,batch-b-retry") + assert len(rows) == 1 + assert rows[0]["score"] == 1.0 diff --git a/tests/test_compare_runs.py b/tests/test_compare_runs.py new file mode 100644 index 0000000..7dbac2c --- /dev/null +++ b/tests/test_compare_runs.py @@ -0,0 +1,83 @@ +"""批次对照器：逐题比较两个批次（默认 kev vs jev），量化空转率与得分差异。""" + +from __future__ import annotations + +import importlib.util +import json +from pathlib import Path + +_ROOT = Path(__file__).resolve().parents[1] + + +def _load(name: str): + path = _ROOT / "tools" / f"{name}.py" + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +batch_summary = _load("batch_summary") +compare_runs = _load("compare_runs") + + +def _make_run(root: Path, name: str, task: str, score: float | None, stop: str, + steps: int, winners: list[str]) -> Path: + episode_dir = root / name / "episodes" / task + episode_dir.mkdir(parents=True, exist_ok=True) + payload = {"task_id": task, "status": "scored" if score is not None else "failed_unscored", + "score": score, "stop_reason": stop, "steps": steps} + (episode_dir / "episode.json").write_text(json.dumps(payload), encoding="utf-8") + (episode_dir / "timing.json").write_text(json.dumps({"wall_seconds": 60}), encoding="utf-8") + (episode_dir / "llm_calls.jsonl").write_text( + json.dumps({"role": "plan_step", + "response": {"usage": {"prompt_tokens": 10, "completion_tokens": 1}}}) + "\n", + encoding="utf-8") + with (episode_dir / "events.jsonl").open("w", encoding="utf-8") as handle: + for operation in winners: + handle.write(json.dumps({"payload": {"winner": {"operation": operation}}}) + "\n") + return root / name + + +def test_winner_stats_separates_noop_from_fallback(tmp_path): + """只有 return_to_planner 是真空转；visual_fallback 走 grounder 产坐标点击。""" + episode = tmp_path / "run" / "episodes" / "001" + episode.mkdir(parents=True) + (episode / "events.jsonl").write_text( + "\n".join(json.dumps({"payload": {"winner": {"operation": op}}}) for op in + ["click", "visual_fallback", "return_to_planner", "click"]) + "\n", + encoding="utf-8") + stats = compare_runs.winner_stats(episode) + assert stats["winners"] == 4 + assert stats["noop"] == 1 + assert stats["fallback"] == 1 + assert stats["noop_rate"] == 0.25 + + +def test_compare_reports_per_task_deltas(tmp_path): + _make_run(tmp_path, "batch-a", "001", 0.0, "no_action_limit", 5, + ["click", "return_to_planner", "return_to_planner"]) + _make_run(tmp_path, "batch-b", "001", 0.8, "done", 12, + ["click", "click", "click", "click"]) + a_rows = compare_runs.enrich(batch_summary.collect_rows(tmp_path, "batch-a"), tmp_path, "batch-a") + b_rows = compare_runs.enrich(batch_summary.collect_rows(tmp_path, "batch-b"), tmp_path, "batch-b") + result = compare_runs.compare(a_rows, b_rows) + assert result["shared"] == 1 + assert result["b_better"] == 1 + assert result["a_better"] == 0 + assert result["b_avg"] > result["a_avg"] + assert result["a_noop"] > result["b_noop"] + report = compare_runs.render(result, "batch-a", "batch-b") + assert "001" in report and "批次对照" in report + + +def test_compare_ignores_tasks_missing_in_one_batch(tmp_path): + _make_run(tmp_path, "batch-a", "001", 1.0, "done", 3, ["click"]) + _make_run(tmp_path, "batch-a", "002", 0.0, "no_action_limit", 2, ["return_to_planner"]) + _make_run(tmp_path, "batch-b", "001", 1.0, "done", 3, ["click"]) + a_rows = compare_runs.enrich(batch_summary.collect_rows(tmp_path, "batch-a"), tmp_path, "batch-a") + b_rows = compare_runs.enrich(batch_summary.collect_rows(tmp_path, "batch-b"), tmp_path, "batch-b") + result = compare_runs.compare(a_rows, b_rows) + assert result["shared"] == 1 + assert len(result["pairs"]) == 1 diff --git a/tools/batch_summary.py b/tools/batch_summary.py new file mode 100644 index 0000000..3b87119 --- /dev/null +++ b/tools/batch_summary.py @@ -0,0 +1,440 @@ +"""批量基线汇总器：把 rjob_logs/ 下多个 run 目录汇成一份 Markdown 基线报告。 + +纯标准库（无新增依赖），只读结果目录、不碰 VM、不调模型。 +聚合内容：得分分布 / 停止原因 / 每题耗时 / 慢模型 token 成本 / 需补跑的环境失败清单。 + +用法： + python3 tools/batch_summary.py --runs-root rjob_logs --prefix osw21-500-kev + python3 tools/batch_summary.py --runs-root rjob_logs --out docs/baseline.md +""" + +from __future__ import annotations + +import argparse +import json +import re +from collections import Counter +from pathlib import Path +from typing import Any + +#: 判定为「环境失败、应补跑」的错误特征（其余失败算模型侧或题目侧，计入基线） +ENV_ERROR_MARKERS = ( + "VM failed to become ready", + "GITLAB_URL and GITLAB_PRIVATE_TOKEN", + "LLM whole-table judgment", + # 判分/评估侧的基础设施故障：缺 judge 凭据、视觉模型调用失败、动作回执缺失 + "No API key found for provider", + "visual model call failed", + "no execution acknowledgement", +) + +#: 评估侧基础设施失败：重跑无望（当前 judge 的媒体上限/能力限制），要换 judge 或绕过才可能通过 +JUDGE_INFRA_MARKERS = ( + "media exceeds size limit", +) + + +def is_judge_infra_failure(row: dict[str, Any]) -> bool: + """判分侧的基础设施失败（如 judge 媒体上限），与"补跑可解"的环境失败区分开。""" + text = f"{row.get('error') or ''} {row.get('exception') or ''}" + return any(marker in text for marker in JUDGE_INFRA_MARKERS) + + +def _read_json(path: Path) -> dict[str, Any]: + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return {} + + +#: 任务域 → 建议的分析方向（画布类缺语义目标，web 类是步内重规划效率问题） +DOMAIN_HINTS = { + "gimp_image": "画布类：无语义目标，需补视觉/结构化线索", + "video_editor": "视频编辑类：时间轴元素无 a11y 语义", + "libreoffice_impress": "画布类：幻灯片元素无 a11y 语义", + "browser_web": "web 类：重点查步内重规划与操作被拒", + "libreoffice_calc": "表格类：a11y 目标清晰，查能否推满分", + "libreoffice_writer": "文档类：查输入/保存路径", + "vscode_ide": "IDE 类：当前最好，验证回退候选改动", + "media_player": "播放器类", + "system_settings": "系统设置类", + "email_client": "邮件客户端类", + "terminal_cli": "终端类", + "other": "其它", +} + +#: 域判定按**题面点名的应用**来分（顺序即优先级），避免泛化关键词误分类 +_DOMAIN_PATTERNS = ( + ("gimp_image", r"\bgimp\b"), + ("video_editor", r"shotcut|video editor|video post-production|footage"), + ("libreoffice_impress", r"\.pptx|slides?\b|presentation\b|deck\b"), + ("libreoffice_calc", r"\.xlsx|\.csv|spreadsheet|\bcalc\b|formula|worksheet"), + ("libreoffice_writer", r"\.docx|\bwriter\b|paragraph|letter|essay|thesis"), + ("vscode_ide", r"vscode|vs code|code editor|repository|\brepo\b|\bgit\b|\bcommit\b"), + ("browser_web", r"http://|https://|\bwebsite\b|browser|chrome|web page|webform|upload|download"), + ("media_player", r"\bvlc\b|media player|playlist|\bmp3\b|\bmp4\b"), + ("email_client", r"thunderbird|\bemail\b|inbox|mail client"), + ("system_settings", r"system settings|bluetooth|\bvolume\b|network settings|display settings"), + ("terminal_cli", r"\bterminal\b|bash script|shell command"), +) + +_WINNER_RE = re.compile(r'"winner":\s*\{[^{}]*?"operation":\s*"([^"]+)"') + + +def domain_of(instruction: str) -> str: + """按题面文本粗判应用域（用于分诊建议，不参与评分）。""" + text = (instruction or "").lower() + for name, pattern in _DOMAIN_PATTERNS: + if re.search(pattern, text): + return name + return "other" + + +def _noop_rate(episode_dir: Path) -> float | None: + """该题的"真空转"占比：return_to_planner 占排序决策的比例。""" + events = episode_dir / "events.jsonl" + if not events.is_file(): + return None + total = noop = 0 + with events.open(encoding="utf-8") as handle: + for line in handle: + match = _WINNER_RE.search(line) + if not match: + continue + total += 1 + if match.group(1) == "return_to_planner": + noop += 1 + return (noop / total) if total else None + + +def _steps_from_events(episode_dir: Path) -> int: + """从事件流回补真实步数（异常路径下 episode.json 的 steps 可能是 0）。""" + events = episode_dir / "events.jsonl" + if not events.is_file(): + return 0 + last = 0 + for line in events.read_text(encoding="utf-8").splitlines(): + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + payload = record.get("payload") or record + step = payload.get("step") + if isinstance(step, int) and step > last: + last = step + return last + + +def _episode_tokens(episode_dir: Path) -> tuple[int, int, int]: + """返回 (调用次数, prompt tokens, completion tokens)；只统计慢模型调用。""" + calls = prompt = completion = 0 + llm_calls = episode_dir / "llm_calls.jsonl" + if not llm_calls.is_file(): + return 0, 0, 0 + for line in llm_calls.read_text(encoding="utf-8").splitlines(): + record = _read_json_line(line) + if not record: + continue + usage = (record.get("response") or {}).get("usage") or record.get("usage") or {} + calls += 1 + prompt += usage.get("prompt_tokens") or 0 + completion += usage.get("completion_tokens") or 0 + return calls, prompt, completion + + +def _read_json_line(line: str) -> dict[str, Any]: + try: + return json.loads(line) + except json.JSONDecodeError: + return {} + + +def collect_rows(runs_root: Path, prefix: str = "") -> list[dict[str, Any]]: + """扫描 runs_root 下匹配的 run 目录，收集每题的汇总行。 + + `prefix` 可为逗号分隔的多个前缀（补跑片前缀常与主批次不同）。 + 同一题出现在多个 run 时取「已出分优先、其次批次时间戳晚」的那条。 + """ + prefixes = [p.strip() for p in prefix.split(",") if p.strip()] + rows: list[dict[str, Any]] = [] + if not runs_root.is_dir(): + return rows + for run_dir in sorted(p for p in runs_root.iterdir() if p.is_dir()): + if prefixes and not any(run_dir.name.startswith(p) for p in prefixes): + continue + episodes = run_dir / "episodes" + if not episodes.is_dir(): + continue + for episode_dir in sorted(p for p in episodes.iterdir() if p.is_dir()): + episode = _read_json(episode_dir / "episode.json") + if not episode: + continue + timing = _read_json(episode_dir / "timing.json") + calls, prompt, completion = _episode_tokens(episode_dir) + steps = episode.get("steps") + if not isinstance(steps, int) or steps <= 0: + # 异常路径下 episode.json 的 steps 记成 0，用事件流回补 + steps = _steps_from_events(episode_dir) + task_meta = _read_json(episode_dir / "task.json") + domain = domain_of(str(task_meta.get("instruction") or "")) + rows.append({ + "run": run_dir.name, + "mtime": (episode_dir / "episode.json").stat().st_mtime, + "task": episode_dir.name, + "score": episode.get("score"), + "status": episode.get("status"), + "stop": episode.get("stop_reason"), + "steps": steps, + "exception": episode.get("exception_type"), + "domain": domain, + "noop_rate": _noop_rate(episode_dir), + "error": (episode.get("error") or "")[:200], + "wall_seconds": timing.get("wall_seconds"), + "calls": calls, + "prompt_tokens": prompt, + "completion_tokens": completion, + }) + best: dict[str, dict[str, Any]] = {} + for row in rows: + current = best.get(row["task"]) + if current is None or _row_rank(row) > _row_rank(current): + best[row["task"]] = row + return sorted(best.values(), key=lambda row: row["task"]) + + +def run_timestamp(run_name: str) -> str: + """从 run-id 里取批次时间戳（形如 20261024-235014），用于判断新旧批次。 + + 不能用文件 mtime：GPFS 上 mtime 精度是秒级，同秒写入的两批排不出先后。 + """ + match = re.search(r"(\d{8}-\d{6})", run_name) + return match.group(1) if match else "" + + +def _row_rank(row: dict[str, Any]) -> tuple[int, str]: + """去重优先级：已出分 > 批次时间戳晚。 + + 补跑的目的是用修复后的配置重测，所以失败题取**最新批次**那次的结果—— + 旧批次里的错误（缺凭据等）已被后续补跑取代，不该盖住新结果。 + （run-id 前缀不同，不能按目录名排；文件 mtime 在 GPFS 上只有秒级精度，也排不出先后。） + """ + return (1 if row["status"] == "scored" else 0, run_timestamp(row["run"])) + + +def is_env_failure(row: dict[str, Any]) -> bool: + """环境/配置类失败（应补跑），区别于模型侧失败（计入基线）。""" + text = f"{row.get('error') or ''} {row.get('exception') or ''}" + return any(marker in text for marker in ENV_ERROR_MARKERS) + + +#: 失败模式标签 → 说明（供报告与后续按任务定位 harness 模块用） +TASK_TAGS = { + "env_failure": "环境/配置失败（需补跑，非模型能力）", + "model_schema": "模型侧异常（schema 不合规、升档重试耗尽）", + "judge_infra": "评估侧基础设施失败（judge 媒体上限等，重跑无望）", + "infeasible": "模型判定任务不可行/放弃（长自然语言 stop_reason）", + "no_action": "空转过多触发 no_action_limit", + "wall_limit": "墙钟预算耗尽", + "max_steps": "步数预算耗尽", + "done_full": "完成且满分", + "done_partial": "完成但部分分", + "done_zero": "完成但零分", +} + + +def classify_task(row: dict[str, Any]) -> str: + """给单题打失败模式标签：便于后续按题定位 harness 模块。 + + 先看停止原因，再看状态：max_steps / wall_time_limit 的题同样会走评分 + （status=scored、score 可能为 0），按状态优先会把它们误归成「完成但零分」。 + """ + stop = str(row.get("stop") or "") + if row["status"] != "scored": + if is_judge_infra_failure(row): + return "judge_infra" + if is_env_failure(row): + return "env_failure" + if row.get("exception") == "SlowModelSchemaError" or "missing required fields" in stop: + return "model_schema" + if len(stop) > 40: # 模型的自然语言理由 = 判定不可行/放弃 + return "infeasible" + if stop == "wall_time_limit": + return "wall_limit" + if stop == "max_steps": + return "max_steps" + if stop == "no_action_limit": + return "no_action" + return "model_schema" + if stop in {"no_action_limit", "wall_time_limit", "max_steps"}: + return {"no_action_limit": "no_action", "wall_time_limit": "wall_limit", + "max_steps": "max_steps"}[stop] + score = row["score"] + if isinstance(score, (int, float)) and score >= 1.0: + return "done_full" + if isinstance(score, (int, float)) and score > 0: + return "done_partial" + return "done_zero" + + +def summarize(rows: list[dict[str, Any]]) -> dict[str, Any]: + """给出行级数据算出的批次级指标。""" + scored = [row for row in rows if row["status"] == "scored"] + scores = [row["score"] for row in scored if isinstance(row["score"], (int, float))] + env_failed = [row for row in rows if row["status"] != "scored" and is_env_failure(row)] + judge_infra = [row for row in rows if row["status"] != "scored" and is_judge_infra_failure(row)] + model_failed = [row for row in rows + if row["status"] != "scored" + and not is_env_failure(row) and not is_judge_infra_failure(row)] + return { + "total": len(rows), + "scored": len(scored), + "env_failed": env_failed, + "judge_infra": judge_infra, + "model_failed": model_failed, + "scores": scores, + "full_hits": sum(1 for score in scores if score >= 1.0), + "partial_hits": sum(1 for score in scores if 0 < score < 1.0), + "zeros": sum(1 for score in scores if score == 0), + "average_scored": (sum(scores) / len(scores)) if scores else None, + "average_all": (sum(scores) / len(rows)) if rows else None, + "stop_reasons": Counter(_brief(row["stop"]) for row in rows), + "wall_seconds": sum(row["wall_seconds"] or 0 for row in rows), + "calls": sum(row["calls"] for row in rows), + "prompt_tokens": sum(row["prompt_tokens"] for row in rows), + "completion_tokens": sum(row["completion_tokens"] for row in rows), + } + + +def _brief(value: Any, limit: int = 28) -> str: + text = str(value or "-") + return text if len(text) <= limit else text[:limit] + "…" + + +def render_markdown(rows: list[dict[str, Any]], stats: dict[str, Any], title: str) -> str: + """把行与统计渲染成 Markdown 报告。""" + lines: list[str] = [f"# {title}", ""] + lines.append( + f"- 已完成 **{stats['total']}** 题（scored {stats['scored']}，" + f"环境失败 {len(stats['env_failed'])}，评估基础设施 {len(stats['judge_infra'])}，" + f"模型侧失败 {len(stats['model_failed'])}）" + ) + if stats["scores"]: + lines.append( + f"- 得分：满分 {stats['full_hits']} / 部分分 {stats['partial_hits']} / 零分 {stats['zeros']}" + ) + lines.append( + f"- 平均分：已出分题 {stats['average_scored']:.4f}；全题面 {stats['average_all']:.4f}" + ) + lines.append(f"- 停止原因：{dict(stats['stop_reasons'])}") + lines.append(f"- 各题耗时合计 {stats['wall_seconds'] / 3600:.1f} 小时") + total_tokens = stats["prompt_tokens"] + stats["completion_tokens"] + lines.append( + f"- 慢模型调用 {stats['calls']} 次，prompt {stats['prompt_tokens']:,} / " + f"completion {stats['completion_tokens']:,} / 合计 {total_tokens:,} tokens" + ) + lines.append("") + lines.append("## 逐题明细") + lines.append("") + lines.append("| 题 | 得分 | 状态 | 停止原因 | 步数 | 墙钟(分) | 空转 | 域 | 调用 | tokens |") + lines.append("|---|---|---|---|---|---|---|---|---|---|") + for row in rows: + score = row["score"] + if score is None: + score_text = "—" + elif isinstance(score, (int, float)): + score_text = f"{score:.4f}" + else: + score_text = str(score) + wall = row["wall_seconds"] + wall_text = f"{wall / 60:.1f}" if isinstance(wall, (int, float)) else "—" + tokens = row["prompt_tokens"] + row["completion_tokens"] + noop = row.get("noop_rate") + noop_text = f"{noop:.0%}" if isinstance(noop, (int, float)) else "—" + lines.append( + f"| {row['task']} | {score_text} | {row['status']} | " + f"{_brief(row['stop'] or row['exception'])} | {row['steps']} | {wall_text} | " + f"{noop_text} | {row.get('domain', 'other')} | " + f"{row['calls']} | {tokens:,} |" + ) + lines.append("") + lines.append("## 失败模式分布（按题分组，供后续定位 harness 模块）") + lines.append("") + grouped: dict[str, list[str]] = {} + for row in rows: + grouped.setdefault(classify_task(row), []).append(row["task"]) + for tag, tasks in sorted(grouped.items(), key=lambda item: -len(item[1])): + lines.append(f"- **{tag}**（{TASK_TAGS.get(tag, tag)}）：{len(tasks)} 题 — {', '.join(tasks)}") + lines.append("") + lines.append("## 分诊建议（按题）") + lines.append("") + lines.append("| 域 | 题数 | 平均空转 | 建议动作 |") + lines.append("|---|---|---|---|") + buckets: dict[str, list[dict[str, Any]]] = {} + for row in rows: + buckets.setdefault(row.get("domain", "other"), []).append(row) + for domain, items in sorted(buckets.items(), key=lambda kv: -len(kv[1])): + noops = [item["noop_rate"] for item in items + if isinstance(item.get("noop_rate"), (int, float))] + avg = sum(noops) / len(noops) if noops else 0.0 + lines.append( + f"| {domain} | {len(items)} | {avg:.0%} | " + f"{DOMAIN_HINTS.get(domain, '查逐题轨迹')} |" + ) + lines.append("") + if stats["env_failed"]: + lines.append("## 需补跑（环境/配置失败，非模型能力）") + lines.append("") + lines.append("| 题 | 异常 | 错误 |") + lines.append("|---|---|---|") + for row in stats["env_failed"]: + lines.append(f"| {row['task']} | {row['exception']} | {row['error'][:90]} |") + lines.append("") + tasks = ",".join(row["task"] for row in stats["env_failed"]) + lines.append("补跑命令：") + lines.append("") + lines.append("```bash") + lines.append(f"TASKS={tasks} MAX_STEPS=500 MAX_WALL=10800 \\") + lines.append(" SLOW_MODEL=minimax-m3.1-flash FAST_MODEL=kev PREFIX=osw21-retry \\") + lines.append(" bash scripts/run_osw21.sh") + lines.append("```") + lines.append("") + if stats["judge_infra"]: + lines.append("## 评估侧基础设施失败（重跑无望，需换 judge 或绕过上限）") + lines.append("") + lines.append("| 题 | 异常 | 错误 |") + lines.append("|---|---|---|") + for row in stats["judge_infra"]: + lines.append(f"| {row['task']} | {row['exception']} | {row['error'][:90]} |") + lines.append("") + if stats["model_failed"]: + lines.append("## 模型侧失败（计入基线，不补跑）") + lines.append("") + for row in stats["model_failed"]: + lines.append(f"- {row['task']}：`{_brief(row['error'] or row['exception'], 120)}`") + lines.append("") + return "\n".join(lines) + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="汇总 rjob_logs 下的批次基线") + parser.add_argument("--runs-root", type=Path, default=Path("rjob_logs")) + parser.add_argument("--prefix", default="", help="只汇总 run 目录名以该前缀开头的批次") + parser.add_argument("--out", type=Path, default=None, help="把 Markdown 报告写到该文件") + parser.add_argument("--title", default="OSW2.1 基线汇总") + args = parser.parse_args(argv) + + rows = collect_rows(args.runs_root, args.prefix) + if not rows: + print(f"没有找到匹配的 run 目录：{args.runs_root}（prefix={args.prefix or '*'}）") + return 1 + stats = summarize(rows) + report = render_markdown(rows, stats, args.title) + print(report) + if args.out: + args.out.write_text(report, encoding="utf-8") + print(f"\n[已写入] {args.out}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/compare_runs.py b/tools/compare_runs.py new file mode 100644 index 0000000..5ca2bd5 --- /dev/null +++ b/tools/compare_runs.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +"""两个批次的逐题对照（默认 kev vs jev）：把「小脑的锅」和「harness 的锅」分开。 + +用法: + python3 tools/compare_runs.py --a osw21-500-kev --b osw21-500-jev-ab + +输出：逐题对照表 + 批次级对比（得分 / 步数 / 空转率 / token 成本）。 +纯标准库、只读结果目录。 +""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import re +from collections import Counter +from pathlib import Path +from typing import Any + +_TOOL = Path(__file__).resolve().parent / "batch_summary.py" +_spec = importlib.util.spec_from_file_location("batch_summary", _TOOL) +batch_summary = importlib.util.module_from_spec(_spec) +assert _spec.loader is not None +_spec.loader.exec_module(batch_summary) + +#: 不产生 UI 动作的赢家（`return_to_planner` 无法编译成动作；`visual_fallback` 会走 grounder 产坐标点击） +NOOP_OPERATIONS = ("return_to_planner",) + +#: 快路径：直接从原始行里抠出 winner 的 operation（整批事件流上 GB 级，逐行 json.loads 太慢） +_WINNER_RE = re.compile(r'"winner":\s*\{[^{}]*?"operation":\s*"([^"]+)"') + + +def winner_stats(episode_dir: Path) -> dict[str, Any]: + """统计一个 episode 的排序赢家分布与空转率。""" + winners = Counter() + events = episode_dir / "events.jsonl" + if events.is_file(): + for line in events.open(encoding="utf-8"): + match = _WINNER_RE.search(line) + if match: + winners[match.group(1)] += 1 + continue + if '"winner"' not in line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + payload = record.get("payload") or record + winner = payload.get("winner") + if isinstance(winner, dict): + winners[str(winner.get("operation"))] += 1 + total = sum(winners.values()) + noop = sum(count for op, count in winners.items() if op in NOOP_OPERATIONS) + fallback = sum(count for op, count in winners.items() if op == "visual_fallback") + return { + "winners": total, + "noop": noop, + "fallback": fallback, + "noop_rate": (noop / total) if total else 0.0, + "fallback_rate": (fallback / total) if total else 0.0, + } + + +def enrich(rows: list[dict[str, Any]], runs_root: Path, prefix: str) -> dict[str, dict[str, Any]]: + """给每行补上所属 run 目录与空转率统计。""" + enriched: dict[str, dict[str, Any]] = {} + for row in rows: + stats = winner_stats(runs_root / row["run"] / "episodes" / row["task"]) + enriched[row["task"]] = {**row, **stats} + return enriched + + +def compare(a_rows: dict[str, dict[str, Any]], b_rows: dict[str, dict[str, Any]]) -> dict[str, Any]: + """两个批次逐题对照，产出可比任务集合与批次级差异。""" + shared = sorted(set(a_rows) & set(b_rows)) + pairs = [] + for task in shared: + a, b = a_rows[task], b_rows[task] + pairs.append({ + "task": task, + "a_score": a["score"], "b_score": b["score"], + "a_stop": a["stop"], "b_stop": b["stop"], + "a_steps": a["steps"], "b_steps": b["steps"], + "a_noop": a["noop_rate"], "b_noop": b["noop_rate"], + "a_tokens": a["prompt_tokens"] + a["completion_tokens"], + "b_tokens": b["prompt_tokens"] + b["completion_tokens"], + }) + + def avg(values: list[float]) -> float: + return sum(values) / len(values) if values else 0.0 + + def scores(rows: dict[str, dict[str, Any]]) -> list[float]: + return [row["score"] for row in rows.values() + if row["status"] == "scored" and isinstance(row["score"], (int, float))] + + return { + "pairs": pairs, + "shared": len(shared), + "a_total": len(a_rows), "b_total": len(b_rows), + "a_avg": avg(scores(a_rows)), "b_avg": avg(scores(b_rows)), + "a_noop": avg([row["noop_rate"] for row in a_rows.values()]), + "b_noop": avg([row["noop_rate"] for row in b_rows.values()]), + "a_steps": avg([row["steps"] or 0 for row in a_rows.values()]), + "b_steps": avg([row["steps"] or 0 for row in b_rows.values()]), + "a_tokens": sum(row["prompt_tokens"] + row["completion_tokens"] for row in a_rows.values()), + "b_tokens": sum(row["prompt_tokens"] + row["completion_tokens"] for row in b_rows.values()), + "b_better": sum(1 for p in pairs + if isinstance(p["b_score"], (int, float)) and isinstance(p["a_score"], (int, float)) + and p["b_score"] > p["a_score"]), + "a_better": sum(1 for p in pairs + if isinstance(p["b_score"], (int, float)) and isinstance(p["a_score"], (int, float)) + and p["a_score"] > p["b_score"]), + } + + +def render(result: dict[str, Any], label_a: str, label_b: str) -> str: + """渲染 Markdown 对照报告。""" + lines = [f"# 批次对照 · {label_a} → {label_b}", ""] + lines.append( + f"- 可比题数 **{result['shared']}**" + f"（{label_a} 共 {result['a_total']} 题，{label_b} 共 {result['b_total']} 题）" + ) + lines.append(f"- 平均分：{label_a} **{result['a_avg']:.4f}** → {label_b} **{result['b_avg']:.4f}**") + lines.append( + f"- 平均空转率（return_to_planner 占排序决策）：" + f"{label_a} **{result['a_noop']:.0%}** → {label_b} **{result['b_noop']:.0%}**" + ) + lines.append(f"- 平均步数：{label_a} {result['a_steps']:.1f} → {label_b} {result['b_steps']:.1f}") + lines.append(f"- token 合计：{label_a} {result['a_tokens']:,} → {label_b} {result['b_tokens']:,}") + lines.append( + f"- 逐题胜负：{label_b} 更好 {result['b_better']} 题 / " + f"{label_a} 更好 {result['a_better']} 题" + ) + lines.append("") + lines.append("| 题 | A 得分 | B 得分 | A 步 | B 步 | A 空转 | B 空转 | A 终态 | B 终态 |") + lines.append("|---|---|---|---|---|---|---|---|---|") + for pair in result["pairs"]: + a_score = "—" if pair["a_score"] is None else f"{pair['a_score']:.3f}" + b_score = "—" if pair["b_score"] is None else f"{pair['b_score']:.3f}" + lines.append( + f"| {pair['task']} | {a_score} | {b_score} | {pair['a_steps']} | {pair['b_steps']} | " + f"{pair['a_noop']:.0%} | {pair['b_noop']:.0%} | " + f"{batch_summary._brief(pair['a_stop'])} | {batch_summary._brief(pair['b_stop'])} |" + ) + lines.append("") + return "\n".join(lines) + + +def _collect(runs_root: Path, prefixes: list[str]) -> dict[str, dict[str, Any]]: + """合并多个前缀的 run 目录（同题取**最新批次**那次：已出分优先，其次 run-id 时间戳晚）。""" + merged: dict[str, dict[str, Any]] = {} + for prefix in prefixes: + rows = enrich(batch_summary.collect_rows(runs_root, prefix), runs_root, prefix) + for task, row in rows.items(): + current = merged.get(task) + if current is None or _rank(row) > _rank(current): + merged[task] = row + return merged + + +def _rank(row: dict[str, Any]) -> tuple[int, str]: + return (1 if row["status"] == "scored" else 0, batch_summary.run_timestamp(row["run"])) + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="两个批次逐题对照") + parser.add_argument("--runs-root", type=Path, default=Path("rjob_logs")) + parser.add_argument("--a", default="osw21-500-kev", help="对照组（基线）前缀，逗号分隔可多个") + parser.add_argument("--b", default="osw21-500-jev-ab", help="实验组前缀，逗号分隔可多个") + parser.add_argument("--out", type=Path, default=None, help="把 Markdown 写到该文件") + args = parser.parse_args(argv) + + a_prefixes = [p.strip() for p in args.a.split(",") if p.strip()] + b_prefixes = [p.strip() for p in args.b.split(",") if p.strip()] + a_rows = _collect(args.runs_root, a_prefixes) + b_rows = _collect(args.runs_root, b_prefixes) + if not a_rows or not b_rows: + print(f"缺少可对照的 run：{args.a}={len(a_rows)} 题，{args.b}={len(b_rows)} 题") + return 1 + result = compare(a_rows, b_rows) + report = render(result, ",".join(a_prefixes), ",".join(b_prefixes)) + print(report) + if args.out: + args.out.write_text(report, encoding="utf-8") + print(f"\n[已写入] {args.out}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/fill_baseline.py b/tools/fill_baseline.py new file mode 100644 index 0000000..310bafa --- /dev/null +++ b/tools/fill_baseline.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +"""把 tools/batch_summary.py 的输出接进基线报告的「自动生成」小节。 + +用法: python3 tools/fill_baseline.py --prefix osw21-500-kev \ + --report docs/baseline-osw21-500-kev.md +""" +from __future__ import annotations + +import argparse +import importlib.util +from pathlib import Path + +_TOOL = Path(__file__).resolve().parent / "batch_summary.py" +_spec = importlib.util.spec_from_file_location("batch_summary", _TOOL) +batch_summary = importlib.util.module_from_spec(_spec) +assert _spec.loader is not None +_spec.loader.exec_module(batch_summary) + +MARKER = "\n" + f"\n\n") + args.report.write_text(f"{head}{header}{report}\n", encoding="utf-8") + print(f"[已更新] {args.report}（{stats['total']} 题，scored {stats['scored']}）") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/rank_probe.py b/tools/rank_probe.py new file mode 100644 index 0000000..be850df --- /dev/null +++ b/tools/rank_probe.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +"""受控对比：同一份 state / 候选 / 截图，分别发给 kev 与 jev，看两个排序服务 +给"回退操作"（visual_fallback / return_to_planner）的打分差异。 + +这是判断"kev 提前判死"根因的决定性实验：输入完全相同，差异只来自排序服务。 + +从真实 episode 里取： + - state：慢模型请求里的 user 文本（目标 + a11y 树） + - options：按 operation 聚合的候选（用 harness 的 OPERATION_DESCRIPTIONS 复刻） +两边发同一份 state/options，比较每个 operation 的得分。 +""" +from __future__ import annotations + +import json +import sys +import urllib.request +from pathlib import Path + +# 与 harness 一致：直连，不走环境里的系统代理 +OPENER = urllib.request.build_opener(urllib.request.ProxyHandler({})) + +REPO = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO / "src")) +from tandem.candidates import OPERATION_DESCRIPTIONS # noqa: E402 + +SA = Path("/mnt/shared-storage-user/evobox-share-gpfs2/xuyuanxun/shared_resources/sa_cua") +# 排序服务宿主机能读的截图视角（harness 的 image_stage.read） +STAGE = Path("/mnt/shared-storage-gpfs2/evobox-share-gpfs2/chenxinquan/tandem_jev_images") +_shots = sorted(STAGE.glob("step_*.png")) if STAGE.is_dir() else [] +IMAGE = str(_shots[-1]) if _shots else "" + + +def service(name: str) -> dict: + return json.loads((SA / "services" / name).read_text(encoding="utf-8")) + + +def pick_episode(task: str) -> tuple[str, list[dict]]: + """取该题最后一个 action 事件里的候选，与慢模型请求里的 user 文本。""" + state = "" + for ep in sorted((REPO / "rjob_logs").glob(f"osw21-500-kev*/episodes/{task}/llm_calls.jsonl")): + for line in ep.read_text(encoding="utf-8").splitlines(): + try: + d = json.loads(line) + except Exception: + continue + if d.get("role") != "plan_step": + continue + for m in (d.get("request", {}).get("payload", {}) or {}).get("messages", []): + if m.get("role") != "user": + continue + c = m.get("content") + text = "" + if isinstance(c, str): + text = c + elif isinstance(c, list): + text = "".join(str(p.get("text") or "") for p in c if p.get("type") == "text") + if len(text) > len(state): + state = text + candidates: list[dict] = [] + for ep in sorted((REPO / "rjob_logs").glob(f"osw21-500-kev*/episodes/{task}/events.jsonl")): + for line in ep.read_text(encoding="utf-8").splitlines(): + try: + d = json.loads(line) + except Exception: + continue + p = d.get("payload") or d + if p.get("kind") == "action" or "candidates" in p: + if p.get("candidates"): + candidates = p["candidates"] + return state, candidates + + +def operation_options(candidates: list[dict]) -> tuple[list[str], list[str]]: + ops: list[str] = [] + for c in candidates: + op = c.get("operation") or c.get("action_type") or "unknown" + if op not in ops: + ops.append(op) + options = [f"Choose operation {op}: {OPERATION_DESCRIPTIONS.get(op, op)}" for op in ops] + return ops, options + + +def ask_kev(state: str, options: list[str], ops: list[str]) -> dict[str, float]: + svc = service("kev.json") + criteria = { + op: (text.split(": ", 1)[1] if ": " in text else text) + for op, text in zip(ops, options, strict=True) + } + payload = { + "state": state, + "model": "kev-latest", + "questions": {"q": {"type": "choice", + "instructions": "Which operation should be performed next?", + "criteria": criteria}}, + } + req = urllib.request.Request(svc["base_url"].rstrip("/") + svc.get("api", "/v1/systemone"), + data=json.dumps(payload).encode("utf-8"), + headers={"Content-Type": "application/json"}) + with OPENER.open(req, timeout=120) as resp: + body = json.load(resp) + probs = body["answers"]["q"]["probabilities"] + return {op: float(probs.get(op, 0.0)) for op in ops} + + +def ask_jev(state: str, options: list[str], ops: list[str]) -> dict[str, float]: + svc = service("qwen_jev.json") + payload = {"state": state, "question": "Which operation should be performed next?", + "options": options, "image_path": IMAGE or ""} + req = urllib.request.Request(svc["base_url"].rstrip("/") + svc.get("api", "/rank"), + data=json.dumps(payload).encode("utf-8"), + headers={"Content-Type": "application/json"}) + with OPENER.open(req, timeout=120) as resp: + body = json.load(resp) + # jev /rank 返回 {"scores": [与 options 对齐的分数], "used_image": bool} + scores = body.get("scores") + if isinstance(scores, list): + return {op: float(scores[i]) for i, op in enumerate(ops) if i < len(scores)} + probs = body.get("probabilities") or {} + return {op: float(probs.get(op, 0.0)) for op in ops} + + +def main() -> None: + args = [a for a in sys.argv[1:] if not a.startswith("--")] + drop_fallback = "--no-fallback" in sys.argv + task = args[0] if args else "004" + state, candidates = pick_episode(task) + if not candidates: + print(f"题 {task} 没找到候选") + return + ops, options = operation_options(candidates) + if "--refine-fallback" in sys.argv: + # 变体：保留回退项，但把描述改写成"最后手段"语义（harness 侧可落地的改法） + refined = { + "visual_fallback": ( + "Choose operation visual_fallback: LAST RESORT ONLY — use this only when " + "no semantic target in the current UI can carry out the goal at all."), + "return_to_planner": ( + "Choose operation return_to_planner: LAST RESORT ONLY — use this only when " + "the current stage assumption is provably wrong and every listed " + "operation would fail."), + } + options = [refined.get(op, opt) + for op, opt in zip(ops, options, strict=True)] + if drop_fallback: + keep = [(op, opt) for op, opt in zip(ops, options, strict=True) + if op not in ("visual_fallback", "return_to_planner")] + ops = [op for op, _ in keep] + options = [opt for _, opt in keep] + print(f"=== 题 {task}：{len(ops)} 个 operation 候选" + f"{'（已剔除回退项）' if drop_fallback else ''}，state {len(state):,} 字符 ===") + for name, fn in (("kev", ask_kev), ("jev", ask_jev)): + try: + scores = fn(state, options, ops) + except Exception as exc: + print(f" {name}: 调用失败 {type(exc).__name__}: {exc}") + continue + ranked = sorted(scores.items(), key=lambda kv: -kv[1]) + print(f" --- {name} ---") + for op, sc in ranked: + mark = "" + if op in ("visual_fallback", "return_to_planner"): + mark = " ← 回退项" + print(f" {op:<22} {sc:.4f}{mark}") + fb = sum(scores.get(o, 0.0) for o in ("visual_fallback", "return_to_planner")) + print(f" 回退项得分合计 {fb:.4f}") + + +if __name__ == "__main__": + main() From 1e44a08ccd739c2428f2b38923334afe0f4c315c Mon Sep 17 00:00:00 2001 From: root Date: Sun, 25 Oct 2026 12:21:54 +0800 Subject: [PATCH 02/31] fix(tools): count every decision without an executable action as a no-op --- tests/test_batch_summary.py | 23 ++++++++++++++++++- tests/test_compare_runs.py | 26 ++++++++++++++++----- tools/batch_summary.py | 29 ++++++++++++++++------- tools/compare_runs.py | 46 +++++++++++++++++++++++-------------- 4 files changed, 92 insertions(+), 32 deletions(-) diff --git a/tests/test_batch_summary.py b/tests/test_batch_summary.py index 977bcbb..206b7b5 100644 --- a/tests/test_batch_summary.py +++ b/tests/test_batch_summary.py @@ -84,7 +84,7 @@ def test_render_markdown_lists_rerun_tasks(tmp_path): }) rows = batch_summary.collect_rows(tmp_path, "batch") report = batch_summary.render_markdown(rows, batch_summary.summarize(rows), "T") - assert "需补跑" in report + assert "未出分的执行侧失败" in report assert "TASKS=003" in report assert "003" in report @@ -189,3 +189,24 @@ def test_collect_rows_accepts_multiple_prefixes(tmp_path): rows = batch_summary.collect_rows(tmp_path, "batch-a,batch-b-retry") assert len(rows) == 1 assert rows[0]["score"] == 1.0 + + +def test_noop_rate_counts_every_decision_without_an_executable_action(tmp_path): + """空转口径：看"动作有没有编译出来"，不是"winner 是不是 return_to_planner"。 + + 真机路径下 grounder 是关的（`tandem_eval/entry.py` 显式传 `grounder=None`）， + 所以 visual_fallback 与 return_to_planner 一样编译不出动作，两者都算真空转。 + """ + episode_dir = tmp_path / "episodes" / "004" + episode_dir.mkdir(parents=True) + events = [ + {"route": "gui", "winner": {"operation": "click"}, "action": {"kind": "gui"}}, + {"route": "gui", "winner": {"operation": "visual_fallback"}, "action": None}, + {"route": "gui", "winner": {"operation": "return_to_planner"}, "action": None}, + {"route": "planner", "plan": {"done": False}}, + ] + (episode_dir / "events.jsonl").write_text( + "\n".join(json.dumps(item, ensure_ascii=False) for item in events) + "\n", + encoding="utf-8", + ) + assert batch_summary._noop_rate(episode_dir) == 2 / 3 diff --git a/tests/test_compare_runs.py b/tests/test_compare_runs.py index 7dbac2c..b90ed58 100644 --- a/tests/test_compare_runs.py +++ b/tests/test_compare_runs.py @@ -36,23 +36,37 @@ def _make_run(root: Path, name: str, task: str, score: float | None, stop: str, encoding="utf-8") with (episode_dir / "events.jsonl").open("w", encoding="utf-8") as handle: for operation in winners: - handle.write(json.dumps({"payload": {"winner": {"operation": operation}}}) + "\n") + handle.write(json.dumps(_gui_event(operation)) + "\n") return root / name -def test_winner_stats_separates_noop_from_fallback(tmp_path): - """只有 return_to_planner 是真空转；visual_fallback 走 grounder 产坐标点击。""" +def _gui_event(operation: str) -> dict: + """造一条 gui 排序事件：回退类（visual_fallback / return_to_planner）没有可执行动作。""" + noop = operation in ("visual_fallback", "return_to_planner") + return { + "route": "gui", + "winner": {"operation": operation}, + "action": None if noop else {"kind": "gui", "action_type": "click"}, + } + + +def test_winner_stats_counts_every_decision_without_an_executable_action(tmp_path): + """空转 = 排序后没有编译出任何动作。 + + 真机路径下 grounder 关闭，所以 visual_fallback 与 return_to_planner + **都是**真空转（旧的"只有 return_to_planner 才算"口径会低估一半）。 + """ episode = tmp_path / "run" / "episodes" / "001" episode.mkdir(parents=True) (episode / "events.jsonl").write_text( - "\n".join(json.dumps({"payload": {"winner": {"operation": op}}}) for op in + "\n".join(json.dumps(_gui_event(op)) for op in ["click", "visual_fallback", "return_to_planner", "click"]) + "\n", encoding="utf-8") stats = compare_runs.winner_stats(episode) assert stats["winners"] == 4 - assert stats["noop"] == 1 + assert stats["noop"] == 2 assert stats["fallback"] == 1 - assert stats["noop_rate"] == 0.25 + assert stats["noop_rate"] == 0.5 def test_compare_reports_per_task_deltas(tmp_path): diff --git a/tools/batch_summary.py b/tools/batch_summary.py index 3b87119..2034c99 100644 --- a/tools/batch_summary.py +++ b/tools/batch_summary.py @@ -78,7 +78,6 @@ def _read_json(path: Path) -> dict[str, Any]: ("terminal_cli", r"\bterminal\b|bash script|shell command"), ) -_WINNER_RE = re.compile(r'"winner":\s*\{[^{}]*?"operation":\s*"([^"]+)"') def domain_of(instruction: str) -> str: @@ -91,18 +90,29 @@ def domain_of(instruction: str) -> str: def _noop_rate(episode_dir: Path) -> float | None: - """该题的"真空转"占比：return_to_planner 占排序决策的比例。""" + """该题的"空转"占比：排序后**没有可执行动作**的决策比例。 + + 口径说明：判空转看的是"动作有没有编译出来"（gui 事件里 `action` 为 null）， + 而不是"winner 是不是回退项"。真机路径下 grounder 是关的 + （`tandem_eval/entry.py` 显式传 `grounder=None`），因此 `visual_fallback` + 与 `return_to_planner` **同样编译不出动作**——两者都是真空转。 + （2026-10-25 修正：旧口径只数 `return_to_planner`，把 44% 的真实空转 + 报成了 22%，低估了一倍。） + """ events = episode_dir / "events.jsonl" if not events.is_file(): return None total = noop = 0 with events.open(encoding="utf-8") as handle: for line in handle: - match = _WINNER_RE.search(line) - if not match: + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + if record.get("route") != "gui" or not record.get("winner"): continue total += 1 - if match.group(1) == "return_to_planner": + if record.get("action") is None: noop += 1 return (noop / total) if total else None @@ -382,7 +392,7 @@ def render_markdown(rows: list[dict[str, Any]], stats: dict[str, Any], title: st ) lines.append("") if stats["env_failed"]: - lines.append("## 需补跑（环境/配置失败，非模型能力）") + lines.append("## 未出分的执行侧失败（已定位并修复，修复后重跑见「优化后对照」）") lines.append("") lines.append("| 题 | 异常 | 错误 |") lines.append("|---|---|---|") @@ -390,7 +400,10 @@ def render_markdown(rows: list[dict[str, Any]], stats: dict[str, Any], title: st lines.append(f"| {row['task']} | {row['exception']} | {row['error'][:90]} |") lines.append("") tasks = ",".join(row["task"] for row in stats["env_failed"]) - lines.append("补跑命令：") + lines.append( + "这类失败已修（CLI 无回执不再判死整题），修复后用同预算重跑，" + "结果记为「优化后对照」，**不并入本基线均分**。复现命令：" + ) lines.append("") lines.append("```bash") lines.append(f"TASKS={tasks} MAX_STEPS=500 MAX_WALL=10800 \\") @@ -399,7 +412,7 @@ def render_markdown(rows: list[dict[str, Any]], stats: dict[str, Any], title: st lines.append("```") lines.append("") if stats["judge_infra"]: - lines.append("## 评估侧基础设施失败（重跑无望，需换 judge 或绕过上限）") + lines.append("## 评估侧基础设施失败（已知限制：judge 媒体上限，需改共享 runtime）") lines.append("") lines.append("| 题 | 异常 | 错误 |") lines.append("|---|---|---|") diff --git a/tools/compare_runs.py b/tools/compare_runs.py index 5ca2bd5..8226efe 100644 --- a/tools/compare_runs.py +++ b/tools/compare_runs.py @@ -24,35 +24,47 @@ assert _spec.loader is not None _spec.loader.exec_module(batch_summary) -#: 不产生 UI 动作的赢家（`return_to_planner` 无法编译成动作；`visual_fallback` 会走 grounder 产坐标点击） -NOOP_OPERATIONS = ("return_to_planner",) +#: 不产生 UI 动作的赢家。真机路径下 grounder 是关闭的 +#: （`tandem_eval/entry.py` 显式传 `grounder=None`），因此 `visual_fallback` +#: 与 `return_to_planner` **都**编译不出动作，两者都是真空转。 +#: （2026-10-25 修正：旧口径只数 `return_to_planner`，把真实空转率低估了一半。） +NOOP_OPERATIONS = ("return_to_planner", "visual_fallback") -#: 快路径：直接从原始行里抠出 winner 的 operation（整批事件流上 GB 级，逐行 json.loads 太慢） +#: 快路径：从原始行里抠出 winner 的 operation（整批事件流上 GB 级，逐行 json.loads 太慢） _WINNER_RE = re.compile(r'"winner":\s*\{[^{}]*?"operation":\s*"([^"]+)"') +#: gui 事件里"没有可执行动作"的标志（json.dumps 的默认分隔符是 ": "） +_NULL_ACTION = '"action": null' def winner_stats(episode_dir: Path) -> dict[str, Any]: - """统计一个 episode 的排序赢家分布与空转率。""" + """统计一个 episode 的排序赢家分布与空转率。 + + 空转口径 = "排序后没有编译出任何动作"的决策占比（gui 事件里 `action` 为 null）， + 而不是"winner 是不是某个回退 operation"——两者在真机路径下等价， + 但前者对将来打开 grounder 的情形仍然正确。 + """ winners = Counter() + noop = 0 events = episode_dir / "events.jsonl" if events.is_file(): for line in events.open(encoding="utf-8"): + if '"winner"' not in line: + continue match = _WINNER_RE.search(line) if match: winners[match.group(1)] += 1 - continue - if '"winner"' not in line: - continue - try: - record = json.loads(line) - except json.JSONDecodeError: - continue - payload = record.get("payload") or record - winner = payload.get("winner") - if isinstance(winner, dict): - winners[str(winner.get("operation"))] += 1 + else: + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + payload = record.get("payload") or record + winner = payload.get("winner") + if isinstance(winner, dict): + winners[str(winner.get("operation"))] += 1 + if _NULL_ACTION in line: + noop += 1 total = sum(winners.values()) - noop = sum(count for op, count in winners.items() if op in NOOP_OPERATIONS) fallback = sum(count for op, count in winners.items() if op == "visual_fallback") return { "winners": total, @@ -124,7 +136,7 @@ def render(result: dict[str, Any], label_a: str, label_b: str) -> str: ) lines.append(f"- 平均分：{label_a} **{result['a_avg']:.4f}** → {label_b} **{result['b_avg']:.4f}**") lines.append( - f"- 平均空转率（return_to_planner 占排序决策）：" + f"- 平均空转率（排序后没有编译出任何动作的决策占比）：" f"{label_a} **{result['a_noop']:.0%}** → {label_b} **{result['b_noop']:.0%}**" ) lines.append(f"- 平均步数：{label_a} {result['a_steps']:.1f} → {label_b} {result['b_steps']:.1f}") From ae7108b167edc58a43e641564a0a8611713b7c17 Mon Sep 17 00:00:00 2001 From: tandem-demo Date: Mon, 26 Oct 2026 14:58:30 +0800 Subject: [PATCH 03/31] =?UTF-8?q?feat(demo):=20=E6=B5=81=E5=BC=8F=E8=AF=BB?= =?UTF-8?q?=E5=8F=96=E5=B1=82=20demo/reader=EF=BC=88=E9=A2=98=E5=8F=B7?= =?UTF-8?q?=E5=BD=92=E4=B8=80=20+=20=E5=A4=A7=E6=96=87=E4=BB=B6=E4=BF=9D?= =?UTF-8?q?=E6=8A=A4=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 结果目录 → 站点数据契约的读取层，拆成 runs/episodes/events/llm/traj/paths 六个模块（老 adapter.py 一个模块同时干三件事，改口径就要动 HTML）。 - llm_calls.jsonl 单题 64MB、整批 39GB：逐行读、只取 usage 与原文， 丢弃 request 里的 base64；单行超 8MB 跳过并计数。 - 题号归一（tasks-004 / 004 → 004），否则跨批次会把同一题当两道。 - 空转口径与 tools/batch_summary 一致（gui 事件 action 为 null）。 - 测试含一条「读取层禁止整文件读」的断言（用只许逐行读的假文件罩住）。 --- demo/reader/__init__.py | 48 +++++++++ demo/reader/episodes.py | 170 +++++++++++++++++++++++++++++++ demo/reader/events.py | 209 ++++++++++++++++++++++++++++++++++++++ demo/reader/llm.py | 146 ++++++++++++++++++++++++++ demo/reader/paths.py | 64 ++++++++++++ demo/reader/runs.py | 95 +++++++++++++++++ demo/reader/traj.py | 105 +++++++++++++++++++ demo/reader/viewmodel.py | 154 ++++++++++++++++++++++++++++ tests/demo/helpers.py | 120 ++++++++++++++++++++++ tests/demo/test_reader.py | 201 ++++++++++++++++++++++++++++++++++++ 10 files changed, 1312 insertions(+) create mode 100644 demo/reader/__init__.py create mode 100644 demo/reader/episodes.py create mode 100644 demo/reader/events.py create mode 100644 demo/reader/llm.py create mode 100644 demo/reader/paths.py create mode 100644 demo/reader/runs.py create mode 100644 demo/reader/traj.py create mode 100644 demo/reader/viewmodel.py create mode 100644 tests/demo/test_reader.py diff --git a/demo/reader/__init__.py b/demo/reader/__init__.py new file mode 100644 index 0000000..d117d33 --- /dev/null +++ b/demo/reader/__init__.py @@ -0,0 +1,48 @@ +"""demo 读取层：结果目录 → 站点数据契约。 + +**边界**（见 `demo/README.md`）：不 import `tandem` / `tandem_eval`、不调模型、不碰 VM。 +唯一输入是 `docs/RESULT_DIR_CONTRACT.md` 描述的结果目录契约。 + +**为什么拆成包**：老 `demo/adapter.py` 一个模块同时干了"读目录 / 算指标 / 拼 HTML"， +三件事互相牵制（改口径就得动 HTML）。这里按**数据流**拆： + + runs.py 发现 run、读 run 级元数据 + episodes.py 读题级终态（分数 / 停止原因 / 评测来源 / 用时） + events.py 流式解析 events.jsonl → 步骤（plan / 决策 / 核验 / replan） + llm.py 流式解析 llm_calls.jsonl → 只取 usage 与原文（丢弃 base64 截图） + traj.py 流式解析 traj.jsonl / a11y_digests.jsonl → 帧索引 + paths.py 题号归一 + 集群路径脱敏 + viewmodel.py 把上面拼成站点唯一数据契约 + aggregate.py 逐题行与汇总（委托 `tools/batch_summary.py`，口径不分叉） + +**硬约束**：`llm_calls.jsonl` 单题 64MB、整批 39GB —— 必须逐行读， +禁止 `read()` / `readlines()`（见 `llm.MAX_LINE_BYTES` 与 `tests/demo/test_reader.py`）。 +""" + +from __future__ import annotations + +from .episodes import EpisodeRef, list_episodes, read_episode_meta +from .events import build_steps, noop_stats, rank_stats, read_events +from .llm import read_llm_texts, read_llm_usage +from .paths import normalize_task_id, scrub_paths +from .runs import RunInfo, discover_runs +from .traj import read_frames +from .viewmodel import build_view_model + +__all__ = [ + "EpisodeRef", + "RunInfo", + "build_steps", + "build_view_model", + "discover_runs", + "list_episodes", + "noop_stats", + "normalize_task_id", + "rank_stats", + "read_episode_meta", + "read_events", + "read_frames", + "read_llm_texts", + "read_llm_usage", + "scrub_paths", +] diff --git a/demo/reader/episodes.py b/demo/reader/episodes.py new file mode 100644 index 0000000..355e48a --- /dev/null +++ b/demo/reader/episodes.py @@ -0,0 +1,170 @@ +"""题级读取：episode 终态（分数 / 停止原因 / 评测来源 / 用时 / 域）。 + +分数有两个来源，必须区分： +- `episode.json` 的 `score` —— 评测器给出的浮点分（未出分时为 `null`）； +- `evaluation.json` 的 `source` —— 评测器身份。 + `official_benchmark_evaluator` = 原生官方评测器（可与官方榜同口径比）； + 其它来源（如 LLM judge）**不能**直接与官方榜比，站点必须显式标注。 +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from .paths import normalize_task_id +from .runs import read_json + + +@dataclass +class EpisodeRef: + """一道题的落盘结果。""" + + run_id: str + task_id: str + dir: Path + score: float | None = None + status: str = "" + stop_reason: str = "" + steps: int = 0 + exception_type: str | None = None + error: str = "" + wall_seconds: float | None = None + evaluator_source: str | None = None + evaluator_raw: float | None = None + domain: str = "" + instruction: str = "" + + @property + def scored(self) -> bool: + return self.score is not None + + @property + def native_evaluator(self) -> bool: + """是否用原生官方评测器（可与官方榜同口径比）。""" + return self.evaluator_source == "official_benchmark_evaluator" + + def to_dict(self) -> dict[str, Any]: + return { + "run_id": self.run_id, + "task_id": self.task_id, + "score": self.score, + "status": self.status, + "stop_reason": self.stop_reason, + "steps": self.steps, + "exception_type": self.exception_type, + "error": self.error, + "wall_seconds": self.wall_seconds, + "evaluator": { + "source": self.evaluator_source, + "raw": self.evaluator_raw, + "native": self.native_evaluator, + }, + "domain": self.domain, + } + + +def list_episodes(run_id: str, episodes_dir: Path) -> list[EpisodeRef]: + """列出 run 下所有题（含未出分与被中断的）。""" + if not episodes_dir.is_dir(): + return [] + refs: list[EpisodeRef] = [] + for episode_dir in sorted(p for p in episodes_dir.iterdir() if p.is_dir()): + episode = read_json(episode_dir / "episode.json") + if not isinstance(episode, dict): + continue + evaluation = read_json(episode_dir / "evaluation.json") or {} + timing = read_json(episode_dir / "timing.json") or {} + task = read_json(episode_dir / "task.json") or {} + raw = evaluation.get("raw") + refs.append( + EpisodeRef( + run_id=run_id, + task_id=normalize_task_id(episode_dir.name), + dir=episode_dir, + score=episode.get("score"), + status=str(episode.get("status") or ""), + stop_reason=str(episode.get("stop_reason") or ""), + steps=int(episode.get("steps") or 0), + exception_type=episode.get("exception_type"), + error=str(episode.get("error") or ""), + wall_seconds=timing.get("wall_seconds"), + evaluator_source=evaluation.get("source"), + evaluator_raw=raw if isinstance(raw, (int, float)) else None, + domain=domain_of(str(task.get("instruction") or "")), + instruction=str(task.get("instruction") or ""), + ) + ) + return refs + + +def read_episode_meta(episode_dir: Path) -> dict[str, Any]: + """题级元数据（薄封装，给 view model 用）。""" + episode = read_json(episode_dir / "episode.json") or {} + evaluation = read_json(episode_dir / "evaluation.json") or {} + return { + "score": episode.get("score"), + "status": episode.get("status"), + "stop_reason": episode.get("stop_reason"), + "steps": episode.get("steps"), + "exception_type": episode.get("exception_type"), + "error": episode.get("error"), + "evaluator": { + "source": evaluation.get("source"), + "raw": evaluation.get("raw"), + }, + } + + +#: 指令关键词 → 域（与 `tools/batch_summary.domain_of` 同口径） +_DOMAIN_RULES: tuple[tuple[str, str], ...] = ( + ("thunderbird", "email_client"), + ("libreoffice writer", "libreoffice_writer"), + ("libreoffice calc", "libreoffice_calc"), + ("libreoffice impress", "libreoffice_impress"), + ("powerpoint", "libreoffice_impress"), + ("gimp", "gimp_image"), + ("photoshop", "gimp_image"), + ("vlc", "media_player"), + ("video", "video_editor"), + ("kdenlive", "video_editor"), + ("chrome", "browser_web"), + ("browser", "browser_web"), + ("firefox", "browser_web"), + ("vscode", "vscode_ide"), + ("visual studio code", "vscode_ide"), + ("settings", "system_settings"), +) + + +def domain_of(instruction: str) -> str: + """按指令文本猜应用域（判不出来归 `other`）。 + + 与 `tools/batch_summary.domain_of` 同一套关键词，站点与分诊报告的域分布才对得上。 + """ + lowered = instruction.lower() + for keyword, domain in _DOMAIN_RULES: + if keyword in lowered: + return domain + return "other" + + +def read_jsonl(path: Path) -> list[dict[str, Any]]: + """读 JSONL（跳过坏行）。仅用于小文件。""" + if not path.is_file(): + return [] + rows: list[dict[str, Any]] = [] + with path.open(encoding="utf-8", errors="replace") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + value = json.loads(line) + except json.JSONDecodeError: + continue + if isinstance(value, dict): + rows.append(value) + return rows diff --git a/demo/reader/events.py b/demo/reader/events.py new file mode 100644 index 0000000..7ef7a59 --- /dev/null +++ b/demo/reader/events.py @@ -0,0 +1,209 @@ +"""流式解析 events.jsonl → 站点步骤序列。 + +`events.jsonl` 是 harness 的事件流，route 取值见 `docs/RESULT_DIR_CONTRACT.md`： +`planner` / `cli` / `gui` / `cli_verified` / `gui_verified` / `terminal` / `done_probe` / +`stage_local` / `stage_progress` / `stage_replan`，外加 runner 写的 `{"kind": "action"}`。 + +**空转口径与 `tools/batch_summary._noop_rate` 完全一致**：判空转看的是 +「动作有没有编译出来」（`gui` 事件里 `action` 为 null），不是「winner 是不是回退项」。 +""" + +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Any + +#: 候选与排序明细的保留条数（站点要看得清，又不能把 2.4MB 事件流整份塞进页面） +MAX_CANDIDATES = 20 +MAX_RANKED = 12 + +#: 步骤里保留的文本长度上限 +MAX_TEXT = 1200 + + +def iter_events(episode_dir: Path) -> Iterator[dict[str, Any]]: + """逐行产出事件（坏行跳过）。""" + path = episode_dir / "events.jsonl" + if not path.is_file(): + return + with path.open(encoding="utf-8", errors="replace") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + if isinstance(record, dict): + yield record + + +def event_kind(record: dict[str, Any]) -> str: + """事件的归一化类型：`route` 优先，其次 runner 写的 `kind`。""" + route = record.get("route") + if isinstance(route, str) and route: + return route + kind = record.get("kind") + return str(kind) if isinstance(kind, str) and kind else "unknown" + + +def read_events(episode_dir: Path) -> list[dict[str, Any]]: + """全部事件（仅用于小文件与测试；站点走 `build_steps`）。""" + return list(iter_events(episode_dir)) + + +def _trim_text(value: Any, limit: int = MAX_TEXT) -> Any: + if isinstance(value, str) and len(value) > limit: + return value[:limit] + "…" + return value + + +def _trim_plan(plan: Any) -> Any: + """计划里只留站点要展示的字段（milestones 里的命令可能很长）。""" + if not isinstance(plan, dict): + return plan + milestones = plan.get("milestones") + trimmed = { + key: plan.get(key) + for key in ("stage_goal", "current_goal", "expected_observation", "route", "task_status", "done") + if key in plan + } + if isinstance(milestones, list): + trimmed["milestones"] = [ + { + "route": item.get("route"), + "current_goal": _trim_text(item.get("current_goal")), + "command": _trim_text(item.get("command"), 400), + } + for item in milestones + if isinstance(item, dict) + ] + return trimmed + + +def _trim_ranking(ranking: Any) -> Any: + if not isinstance(ranking, dict): + return None + out: dict[str, Any] = {} + for key in ("operation_margin", "target_margin", "latency_ms", "used_image", "winner_id"): + if key in ranking: + out[key] = ranking[key] + for key in ("operation_ranked", "target_ranked", "action_ranked"): + ranked = ranking.get(key) + if isinstance(ranked, list): + out[key] = ranked[:MAX_RANKED] + request = ranking.get("operation_request") or ranking.get("target_request") + if isinstance(request, dict): + keep = ("operation", "target", "operation_options") + out["request"] = {k: v for k, v in request.items() if k in keep} + return out + + +def build_steps(episode_dir: Path) -> list[dict[str, Any]]: + """把事件流整形成站点要的步骤序列。""" + steps: list[dict[str, Any]] = [] + for record in iter_events(episode_dir): + kind = event_kind(record) + step_no = record.get("step") + step: dict[str, Any] = { + "step": step_no if isinstance(step_no, int) else len(steps), + "kind": kind, + } + if kind == "planner": + step["plan"] = _trim_plan(record.get("plan")) + elif kind == "stage_local": + step["stage_goal"] = _trim_text(record.get("stage_goal")) + step["milestone"] = record.get("milestone") + step["route"] = record.get("route") + elif kind == "stage_progress": + step["stage_goal"] = _trim_text(record.get("stage_goal")) + step["milestone"] = record.get("milestone") + step["source"] = record.get("source") + elif kind == "stage_replan": + step["reason"] = record.get("reason") + step["stage_goal"] = _trim_text(record.get("stage_goal")) + step["milestone"] = record.get("milestone") + elif kind == "gui": + candidates = record.get("candidates") or [] + step["candidates"] = [ + { + "id": item.get("id"), + "operation": item.get("operation"), + "description": _trim_text(item.get("description"), 200), + "target_id": item.get("target_id"), + "source": item.get("source"), + "semantic_key": (item.get("metadata") or {}).get("semantic_key"), + } + for item in candidates[:MAX_CANDIDATES] + if isinstance(item, dict) + ] + step["n_candidates"] = len(candidates) + step["ranking"] = _trim_ranking(record.get("ranking")) + step["winner"] = record.get("winner") + step["action"] = record.get("action") + step["blocked_action"] = record.get("blocked_action") + step["noop"] = record.get("action") is None + elif kind == "cli": + step["command"] = _trim_text(record.get("command"), 2000) + step["action"] = record.get("action") + elif kind in {"gui_verified", "cli_verified"}: + verification = record.get("verification") or {} + step["verification"] = { + "outcome": verification.get("outcome"), + "evidence": _trim_text(verification.get("evidence")), + "state_changed": verification.get("state_changed"), + "requirement_checks": verification.get("requirement_checks") or [], + } + step["execution"] = record.get("execution") + elif kind == "terminal": + step["planner"] = _trim_plan(record.get("planner")) + step["completion_check"] = record.get("completion_check") + elif kind == "action": + step["action"] = record.get("action") + steps.append(step) + return steps + + +def noop_stats(episode_dir: Path) -> dict[str, Any]: + """空转口径（与 `tools/batch_summary._noop_rate` 一致）。""" + total = noop = 0 + for record in iter_events(episode_dir): + if record.get("route") != "gui" or not record.get("winner"): + continue + total += 1 + if record.get("action") is None: + noop += 1 + return {"decisions": total, "noop": noop, "noop_rate": (noop / total) if total else None} + + +def rank_stats(episode_dir: Path) -> dict[str, Any]: + """快模型排序侧的统计：调用次数与总耗时（毫秒）。 + + 快模型是本地 GPU 服务，**不折美元**——这里只给"多少次、多慢"。 + """ + calls = 0 + latency_total = 0.0 + for record in iter_events(episode_dir): + if record.get("route") != "gui": + continue + ranking = record.get("ranking") + if not isinstance(ranking, dict): + continue + calls += 1 + latency = ranking.get("latency_ms") + if isinstance(latency, (int, float)): + latency_total += float(latency) + return {"rank_calls": calls, "rank_ms_total": round(latency_total, 1)} + + +def steps_from_events(episode_dir: Path) -> int: + """从事件流回补真实步数（异常路径下 episode.json 的 steps 可能是 0）。""" + last = 0 + for record in iter_events(episode_dir): + step = record.get("step") + if isinstance(step, int) and step > last: + last = step + return last diff --git a/demo/reader/llm.py b/demo/reader/llm.py new file mode 100644 index 0000000..1a65160 --- /dev/null +++ b/demo/reader/llm.py @@ -0,0 +1,146 @@ +"""流式解析 llm_calls.jsonl → token 用量 + 慢模型原文。 + +**这是全站最重的一个文件**：单题 64MB（base64 截图内嵌在 `request` 里）， +整批 39GB。因此： + +- 逐行读，**禁止 `read()` / `readlines()`**（`tests/demo/test_reader.py` 里有断言）； +- 只从 `response` 取 `usage` 与 `choices[0].message.content`，**不碰 `request`**； +- 单行超过 `MAX_LINE_BYTES` 直接跳过并计数（不让一行坏数据吃掉内存）。 + +口径与 `tools/batch_summary._episode_tokens` 一致：每行算一次调用， +prompt/completion 取 `response.usage`（缺失则回退顶层 `usage`）。 +""" + +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Any + +#: 单行上限（超过视为异常行，跳过并计数） +MAX_LINE_BYTES = 8 * 1024 * 1024 + +#: 单题最多保留的原文条数与每条长度 +MAX_TEXTS = 8 +MAX_TEXT_CHARS = 4000 + + +def iter_llm_calls(episode_dir: Path) -> Iterator[dict[str, Any]]: + """逐行产出慢模型调用记录（已丢弃 `request` 里的 base64）。""" + path = episode_dir / "llm_calls.jsonl" + if not path.is_file(): + return + with path.open(encoding="utf-8", errors="replace") as handle: + for line in handle: + if len(line) > MAX_LINE_BYTES: + continue + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + if not isinstance(record, dict): + continue + record.pop("request", None) # base64 截图在这里，直接丢掉 + yield record + + +def _usage_of(record: dict[str, Any]) -> dict[str, Any]: + usage = (record.get("response") or {}).get("usage") or record.get("usage") or {} + return usage if isinstance(usage, dict) else {} + + +def read_llm_usage(episode_dir: Path) -> dict[str, Any]: + """慢模型 token 用量：总数 + 按 role 拆分。 + + `calls` / `prompt_tokens` / `completion_tokens` 与 `tools/batch_summary` 逐位一致 + （一致性测试守着这条线）。 + """ + total = { + "calls": 0, + "prompt_tokens": 0, + "completion_tokens": 0, + "cached_tokens": 0, + "reasoning_tokens": 0, + } + by_role: dict[str, dict[str, int]] = {} + for record in iter_llm_calls(episode_dir): + role = str(record.get("role") or "unknown") + usage = _usage_of(record) + bucket = by_role.setdefault( + role, {key: 0 for key in ("calls", "prompt_tokens", "completion_tokens", + "cached_tokens", "reasoning_tokens")} + ) + bucket["calls"] += 1 + total["calls"] += 1 + for key in ("prompt_tokens", "completion_tokens"): + value = usage.get(key) + if isinstance(value, (int, float)): + bucket[key] += int(value) + total[key] += int(value) + details = usage.get("completion_tokens_details") or {} + reasoning = details.get("reasoning_tokens") + if isinstance(reasoning, (int, float)): + bucket["reasoning_tokens"] += int(reasoning) + total["reasoning_tokens"] += int(reasoning) + prompt_details = usage.get("prompt_tokens_details") or {} + cached = prompt_details.get("cached_tokens") + if isinstance(cached, (int, float)): + bucket["cached_tokens"] += int(cached) + total["cached_tokens"] += int(cached) + return {"total": total, "by_role": by_role} + + +def read_llm_texts( + episode_dir: Path, *, limit: int = MAX_TEXTS, max_chars: int = MAX_TEXT_CHARS +) -> list[dict[str, Any]]: + """慢模型原文（**只搬运，不改写**）。 + + 保留最先出现的若干条调用原文，供播放器上轨流式打字。 + 每条带 `truncated` 标记，UI 要如实说明"原文被截断"。 + """ + texts: list[dict[str, Any]] = [] + for record in iter_llm_calls(episode_dir): + response = record.get("response") or {} + choices = response.get("choices") or [] + if not choices: + continue + message = choices[0].get("message") or {} + content = message.get("content") + if not isinstance(content, str) or not content.strip(): + continue + texts.append( + { + "role": str(record.get("role") or "unknown"), + "attempt": record.get("attempt"), + "time": record.get("time"), + "text": content[:max_chars], + "truncated": len(content) > max_chars, + } + ) + if len(texts) >= limit: + break + return texts + + +def read_llm_errors(episode_dir: Path, *, limit: int = 20) -> list[dict[str, Any]]: + """带 `api_exception` 的调用（模型侧失败的证据）。""" + errors: list[dict[str, Any]] = [] + for record in iter_llm_calls(episode_dir): + exception = record.get("api_exception") + if not exception: + continue + errors.append( + { + "role": record.get("role"), + "attempt": record.get("attempt"), + "time": record.get("time"), + "exception": exception, + } + ) + if len(errors) >= limit: + break + return errors diff --git a/demo/reader/paths.py b/demo/reader/paths.py new file mode 100644 index 0000000..96f2024 --- /dev/null +++ b/demo/reader/paths.py @@ -0,0 +1,64 @@ +"""题号归一 + 集群路径脱敏。 + +**题号**：不同批次的 episode 目录命名不一致——仓库批次用 `tasks-004`， +stage+memory 批次用 `004`。站点与跨批次对比必须用同一套题号，否则 `004` 与 +`tasks-004` 会被当成两道题。 + +**脱敏**：发布到内网共享目录前，集群挂载路径与账号名必须替换成占位符 +（`demo/README.md` 的发布纪律）。VM 内的路径（如 `/home/user/Desktop/x.pptx`） +属任务语义，**保留**——那是评测对象的一部分。 +""" + +from __future__ import annotations + +import re +from typing import Any + +#: `tasks-004` / `task_004` / `tasks_004` → 去掉前缀 +_TASK_PREFIX = re.compile(r"^tasks?[-_]") + +#: 集群挂载路径 → `/<末段文件名>` +_CLUSTER_PATH = re.compile(r"/mnt/shared-storage-(?:user|gpfs2)/[^\s\"'<>]*") + +#: 账号目录段（仅在明确的集群路径上下文里替换，避免误伤任务文本） +_ACCOUNT = re.compile(r"(?<=/)[a-z][a-z0-9_-]{2,}(?=/)") + + +def normalize_task_id(raw: str) -> str: + """把 episode 目录名 / 题号统一成三位数字字符串。 + + >>> normalize_task_id("tasks-004") + '004' + >>> normalize_task_id("004") + '004' + >>> normalize_task_id("4") + '004' + """ + text = _TASK_PREFIX.sub("", str(raw).strip()) + if text.isdigit(): + return text.zfill(3) + return text + + +def scrub_paths(value: Any) -> Any: + """把集群挂载路径换成占位符（保留末段文件名，便于对照）。""" + if isinstance(value, str): + return _CLUSTER_PATH.sub(lambda m: "/" + m.group(0).rsplit("/", 1)[-1], value) + if isinstance(value, dict): + return {key: scrub_paths(item) for key, item in value.items()} + if isinstance(value, list): + return [scrub_paths(item) for item in value] + return value + + +def scrub_account(value: Any) -> Any: + """把路径里的账号目录段替换成 ``（只作用于路径串，不动任务指令）。""" + if isinstance(value, str): + if "/" not in value or value.startswith("/home/user"): + return value + return _ACCOUNT.sub("", value) + if isinstance(value, dict): + return {key: scrub_account(item) for key, item in value.items()} + if isinstance(value, list): + return [scrub_account(item) for item in value] + return value diff --git a/demo/reader/runs.py b/demo/reader/runs.py new file mode 100644 index 0000000..a10200f --- /dev/null +++ b/demo/reader/runs.py @@ -0,0 +1,95 @@ +"""run 级读取：发现 run 目录、读 run 级元数据。 + +run 目录契约（`docs/RESULT_DIR_CONTRACT.md`）： +`summary.json` / `heartbeat.json` / `code_version.json` / `episodes//…`。 +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + + +@dataclass +class RunInfo: + """一个 run（= 一次提交的一组题）。""" + + run_id: str + path: Path + benchmark: str = "" + harness: dict[str, Any] = field(default_factory=dict) + total: int = 0 + scored: int = 0 + failed_unscored: int = 0 + average_score: float | None = None + code_version: str | None = None + inner_image: str | None = None + has_summary: bool = False + + @property + def episodes_dir(self) -> Path: + return self.path / "episodes" + + def to_dict(self) -> dict[str, Any]: + return { + "run_id": self.run_id, + "benchmark": self.benchmark, + "harness": self.harness, + "total": self.total, + "scored": self.scored, + "failed_unscored": self.failed_unscored, + "average_score": self.average_score, + "code_version": self.code_version, + "inner_image": self.inner_image, + } + + +def read_json(path: Path) -> Any: + """读单个 JSON；不存在或坏行返回 None（契约允许文件缺失）。""" + if not path.is_file(): + return None + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return None + + +def discover_runs(runs_root: Path, benchmark: str | None = None) -> list[RunInfo]: + """扫 `runs_root/*/summary.json`，返回 run 列表（按 run_id 排序）。 + + `benchmark` 过滤按 `summary.json.benchmark` 判；没有 summary 的目录 + （被中途停掉的 run）也会被收进来，但 `has_summary=False`—— + 站点要能显示"这个 run 跑到一半被停了"，而不是当作不存在。 + """ + if not runs_root.is_dir(): + return [] + runs: list[RunInfo] = [] + for run_dir in sorted(p for p in runs_root.iterdir() if p.is_dir()): + summary = read_json(run_dir / "summary.json") + code_version = read_json(run_dir / "code_version.json") or {} + info = RunInfo( + run_id=run_dir.name, + path=run_dir, + benchmark=str((summary or {}).get("benchmark") or ""), + harness=dict((summary or {}).get("harness") or {}), + total=int((summary or {}).get("total") or 0), + scored=int((summary or {}).get("scored") or 0), + failed_unscored=int((summary or {}).get("failed_unscored") or 0), + average_score=(summary or {}).get("average_score"), + code_version=code_version.get("git_describe"), + inner_image=((code_version.get("images") or {}) or {}).get("inner"), + has_summary=summary is not None, + ) + if benchmark and info.benchmark and info.benchmark != benchmark: + continue + runs.append(info) + return runs + + +def run_episode_dirs(run: RunInfo) -> list[Path]: + """run 下的 episode 目录（按题号排序）。""" + if not run.episodes_dir.is_dir(): + return [] + return sorted(p for p in run.episodes_dir.iterdir() if p.is_dir()) diff --git a/demo/reader/traj.py b/demo/reader/traj.py new file mode 100644 index 0000000..85ce203 --- /dev/null +++ b/demo/reader/traj.py @@ -0,0 +1,105 @@ +"""流式解析 traj.jsonl / a11y_digests.jsonl → 帧序列与逐步摘要。 + +`traj.jsonl` 每行一次观察（截图 + a11y + 预算 + 上一次回执）。 +`a11y_digests.jsonl` 每行一个 a11y 摘要（含 `node_count`）—— +候选空间漏斗的第一级（a11y 节点数 → 候选数 → winner）就靠它。 +""" + +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Any + + +def iter_traj(episode_dir: Path) -> Iterator[dict[str, Any]]: + """逐行产出 traj 记录（坏行跳过）。""" + path = episode_dir / "traj.jsonl" + if not path.is_file(): + return + with path.open(encoding="utf-8", errors="replace") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + if isinstance(record, dict): + yield record + + +def read_node_counts(episode_dir: Path) -> dict[int, int]: + """step_idx → a11y 节点数。""" + counts: dict[int, int] = {} + path = episode_dir / "a11y_digests.jsonl" + if not path.is_file(): + return counts + with path.open(encoding="utf-8", errors="replace") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + step = record.get("step_idx") + count = record.get("node_count") + if isinstance(step, int) and isinstance(count, int): + counts[step] = count + return counts + + +def read_frames(episode_dir: Path) -> list[dict[str, Any]]: + """帧索引：截图路径 + 校验和 + a11y 节点数。 + + 截图本体不复制进站点数据（站点构建时另行缩放），这里只给路径与校验和。 + """ + node_counts = read_node_counts(episode_dir) + frames: list[dict[str, Any]] = [] + for record in iter_traj(episode_dir): + observation = record.get("observation") or {} + screenshot = observation.get("screenshot") or {} + tree = observation.get("accessibility_tree") or {} + step = record.get("step_idx") + frames.append( + { + "step_idx": step, + "wall_time": record.get("wall_time"), + "screenshot": { + "path": screenshot.get("path"), + "sha256": screenshot.get("sha256"), + "width": screenshot.get("width"), + "height": screenshot.get("height"), + }, + "node_count": tree.get("node_count") or node_counts.get(step), + "a11y_present": tree.get("present"), + } + ) + return frames + + +def read_budget(episode_dir: Path) -> dict[str, Any]: + """最后一次观察里的预算快照（步数/墙钟剩余）。""" + budget: dict[str, Any] = {} + for record in iter_traj(episode_dir): + observation = record.get("observation") or {} + value = observation.get("budget") + if isinstance(value, dict): + budget = value + return budget + + +def read_receipts(episode_dir: Path) -> dict[int, dict[str, Any]]: + """step_idx → 上一次动作的回执（判定动作是否被环境确认）。""" + receipts: dict[int, dict[str, Any]] = {} + for record in iter_traj(episode_dir): + observation = record.get("observation") or {} + receipt = observation.get("last_receipt") + step = record.get("step_idx") + if isinstance(step, int) and isinstance(receipt, dict): + receipts[step] = receipt + return receipts diff --git a/demo/reader/viewmodel.py b/demo/reader/viewmodel.py new file mode 100644 index 0000000..56730f0 --- /dev/null +++ b/demo/reader/viewmodel.py @@ -0,0 +1,154 @@ +"""把读取层拼成**站点唯一数据契约**（episode view model）。 + +契约形状见 `docs/DEMO_REDESIGN.md` §4.2。站点、导出器、实时 API 三个出口 +都只认这个形状——换数据源时只改 reader，不动前端。 + +单题目标 ≤300KB：慢模型原文每题 ≤8 条 × ≤4KB，候选只留前 20 个。 +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from .episodes import EpisodeRef +from .events import build_steps, noop_stats, rank_stats, steps_from_events +from .llm import read_llm_errors, read_llm_texts, read_llm_usage +from .paths import scrub_paths +from .traj import read_budget, read_frames + +#: 没有价格表时的兜底：只报 token，不报美元（红线 3：没有价格就不许出现美元数） +NO_PRICE = {"snapshot": None, "usd_estimate": None} + + +def estimate_usd(usage_total: dict[str, Any], price: dict[str, Any] | None) -> dict[str, Any]: + """按价格表估算美元成本。 + + `price` 形如 `{"input_per_1m": 0.15, "output_per_1m": 0.60, "cached_per_1m": 0.015, + "model": "...", "snapshot": "2026-10-23"}`。缺表 → 不出美元数。 + """ + if not price: + return dict(NO_PRICE) + prompt = int(usage_total.get("prompt_tokens") or 0) + cached = int(usage_total.get("cached_tokens") or 0) + completion = int(usage_total.get("completion_tokens") or 0) + fresh_input = max(prompt - cached, 0) + usd = ( + fresh_input / 1e6 * float(price.get("input_per_1m") or 0.0) + + cached / 1e6 * float(price.get("cached_per_1m") or 0.0) + + completion / 1e6 * float(price.get("output_per_1m") or 0.0) + ) + return { + "usd_estimate": round(usd, 6), + "snapshot": price.get("snapshot"), + "model": price.get("model"), + } + + +def build_view_model( + ref: EpisodeRef, + *, + price: dict[str, Any] | None = None, + sai: dict[str, Any] | None = None, + include_text: bool = True, + max_candidates_note: str = "", +) -> dict[str, Any]: + """一道题的 view model。""" + episode_dir = ref.dir + steps = build_steps(episode_dir) + usage = read_llm_usage(episode_dir) + usage_total = usage["total"] + noop = noop_stats(episode_dir) + fast = rank_stats(episode_dir) + + real_steps = ref.steps if ref.steps > 0 else steps_from_events(episode_dir) + frames = read_frames(episode_dir) + texts = read_llm_texts(episode_dir) if include_text else [] + errors = read_llm_errors(episode_dir) + + model: dict[str, Any] = { + "task_id": ref.task_id, + "run_id": ref.run_id, + "status": ref.status, + "score": ref.score, + "stop_reason": ref.stop_reason, + "steps": real_steps, + "wall_seconds": ref.wall_seconds, + "domain": ref.domain, + "instruction": ref.instruction, + "noop_rate": noop["noop_rate"], + "decisions": noop["decisions"], + "noop_decisions": noop["noop"], + "evaluator": { + "source": ref.evaluator_source, + "raw": ref.evaluator_raw, + "native": ref.native_evaluator, + }, + "tokens": { + "prompt": usage_total["prompt_tokens"], + "completion": usage_total["completion_tokens"], + "cached": usage_total["cached_tokens"], + "reasoning": usage_total["reasoning_tokens"], + "calls": usage_total["calls"], + "by_role": usage["by_role"], + }, + "fast_model": fast, + "steps_detail": steps, + "llm_texts": texts, + "llm_errors": errors, + "replans": [s for s in steps if s.get("kind") == "stage_replan"], + "requirements": _requirements(steps), + "frames": frames, + "budget": read_budget(episode_dir), + "error": ref.error, + "exception_type": ref.exception_type, + } + model["tokens"].update(estimate_usd(usage_total, price)) + if sai: + model["sai"] = sai + if max_candidates_note: + model["note"] = max_candidates_note + return scrub_paths(model) + + +def _requirements(steps: list[dict[str, Any]]) -> list[dict[str, Any]]: + """需求核验清单：把各次 verify 的 requirement_checks 合并去重。 + + 同一需求在多次核验里可能反复出现，这里保留**最后一次**状态与证据。 + """ + merged: dict[str, dict[str, Any]] = {} + for step in steps: + verification = step.get("verification") or {} + for check in verification.get("requirement_checks") or []: + if not isinstance(check, dict): + continue + key = str(check.get("id") or check.get("name") or "") + if not key: + continue + merged[key] = { + "id": key, + "status": check.get("status"), + "evidence": check.get("evidence"), + "step": step.get("step"), + } + return list(merged.values()) + + +def frame_source(run_id: str, task_id: str, step_idx: int | None) -> str: + """站点内帧的相对路径（构建时生成缩放帧到 `assets/frames///`）。""" + name = f"step_{int(step_idx):02d}.jpg" if isinstance(step_idx, int) else "step_00.jpg" + return f"assets/frames/{run_id}/{task_id}/{name}" + + +def attach_frame_paths(model: dict[str, Any]) -> dict[str, Any]: + """给 view model 的帧补上站点内相对路径。""" + run_id = model.get("run_id") or "" + task_id = model.get("task_id") or "" + for frame in model.get("frames") or []: + frame["src"] = frame_source(run_id, task_id, frame.get("step_idx")) + return model + + +def source_dir_hint(path: Path) -> str: + """源目录的可读提示（发布前脱敏用）。""" + return scrub_paths(str(path)) diff --git a/tests/demo/helpers.py b/tests/demo/helpers.py index 1c0b555..f9958ee 100644 --- a/tests/demo/helpers.py +++ b/tests/demo/helpers.py @@ -168,3 +168,123 @@ def make_run(root: Path, run_id: str, tasks: list[dict]) -> Path: {"run_id": run_id, "benchmark": "osworld2", "count": len(entries), "tasks": entries}, ) return run_dir + + +# --------------------------------------------------------------------------- +# 现行契约（episode.json / evaluation.json / events.jsonl / llm_calls.jsonl） +# 的合成 run：给 `demo.reader` 用。旧契约的 `make_run` 保留给老测试。 +# --------------------------------------------------------------------------- + + +def make_contract_episode( + run_dir: Path, + *, + task_dir: str = "tasks-004", + score: float | None = 0.6556, + stop: str = "done", + steps: int = 3, +) -> Path: + """造一个符合现行结果目录契约的 episode 目录。""" + episode_dir = run_dir / "episodes" / task_dir + write_json( + episode_dir / "episode.json", + { + "task_id": "004", + "harness": "tandem 0.1.0", + "stop_reason": stop, + "steps": steps, + "status": "scored" if score is not None else "failed_unscored", + "score": score, + "exception_type": None if score is not None else "ValueError", + }, + ) + write_json( + episode_dir / "evaluation.json", + {"source": "official_benchmark_evaluator", "raw": score, "score": score}, + ) + write_json(episode_dir / "timing.json", {"wall_seconds": 2944.5, "status": "scored"}) + write_json( + episode_dir / "task.json", + {"id": "004", "instruction": "Update only these new slides in the deck."}, + ) + write_jsonl( + episode_dir / "events.jsonl", + [ + {"route": "planner", "step": 0, + "plan": {"stage_goal": "查样式", "current_goal": "看页脚"}}, + { + "route": "gui", + "step": 1, + "candidates": [{"id": "click:a11y:t0:1", "operation": "click", "source": "a11y"}], + "ranking": {"operation_margin": 0.31, "target_margin": 0.12, "latency_ms": 42.0, + "used_image": True, + "operation_ranked": [{"operation": "click", "score": 0.7}]}, + "winner": {"id": "click:a11y:t0:1", "operation": "click"}, + "action": {"operation": "click"}, + }, + {"route": "gui", "step": 2, "candidates": [], "winner": {"id": "x"}, "action": None}, + { + "route": "gui_verified", + "step": 3, + "verification": { + "outcome": "not_achieved", + "evidence": "没变化", + "state_changed": False, + "requirement_checks": [ + {"id": "font_name", "status": "failed", "evidence": "字体没改"} + ], + }, + }, + {"route": "stage_replan", "step": 4, "reason": "execution_failed_or_unverified", + "stage_goal": "重查样式", "milestone": 0}, + {"kind": "action", "step": 5, "action": {"operation": "click"}}, + ], + ) + write_jsonl( + episode_dir / "llm_calls.jsonl", + [ + {"role": "plan_step", "attempt": 1, "time": 1.0, + "request": {"messages": [{"content": "x" * 5000}]}, + "response": {"choices": [{"message": {"content": "{\"current_goal\": \"看页脚\"}"}}], + "usage": {"prompt_tokens": 100, "completion_tokens": 50, + "completion_tokens_details": {"reasoning_tokens": 20}, + "prompt_tokens_details": {"cached_tokens": 7}}}}, + {"role": "plan_step", "attempt": 2, "time": 2.0, + "request": {"messages": []}, + "response": {"choices": [{"message": {"content": "第二段原文"}}], + "usage": {"prompt_tokens": 10, "completion_tokens": 5}}}, + ], + ) + write_jsonl( + episode_dir / "traj.jsonl", + [ + {"step_idx": 1, "wall_time": 100.0, + "observation": { + "screenshot": {"path": "/mnt/shared-storage-user/x/step_00.png", + "sha256": "ab" * 32, "width": 1920, "height": 1080}, + "accessibility_tree": {"present": True, "node_count": 4948}, + "budget": {"steps_remaining": 10, "wall_seconds_remaining": 100.0}}, + "action": {"operation": "click"}}, + ], + ) + write_jsonl(episode_dir / "a11y_digests.jsonl", + [{"step_idx": 1, "sha256": "cd" * 32, "node_count": 4948}]) + return episode_dir + + +def make_contract_run(root: Path, run_id: str = "osw21-run-1") -> Path: + """造一个符合现行契约的 run 目录（osworld21 基准、stage 模式）。""" + run_dir = root / run_id + write_json( + run_dir / "summary.json", + {"run_id": run_id, "benchmark": "osworld21", + "harness": {"name": "tandem", "mode": "stage", + "models": {"slow": "m3.1-flash", "fast": "jev"}}, + "total": 1, "scored": 1, "failed_unscored": 0, "average_score": 0.6556, "tasks": []}, + ) + write_json( + run_dir / "code_version.json", + {"git_describe": "unknown(experiment)", "images": {"inner": "weavebench@sha256:0e6497a9"}}, + ) + make_contract_episode(run_dir) + return run_dir diff --git a/tests/demo/test_reader.py b/tests/demo/test_reader.py new file mode 100644 index 0000000..4dc7153 --- /dev/null +++ b/tests/demo/test_reader.py @@ -0,0 +1,201 @@ +"""demo 读取层单测：合成结果目录 → view model。 + +不依赖 `rjob_logs/` 里的真实 run（CI 上没有），也不 import `tandem` / +`tandem_eval`——demo 与 harness 完全解耦，这条边界本身就是被测项之一。 + +**最要紧的一条**：`test_llm_reader_never_reads_whole_file` 用一个"只许逐行读"的 +假文件把 `llm_calls.jsonl`（单题 64MB、整批 39GB）罩住——读取层一旦整文件读， +测试立刻炸。 +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from helpers import make_contract_episode as make_episode # noqa: E402 +from helpers import make_contract_run as make_run # noqa: E402 + +from demo.reader import ( # noqa: E402 + build_steps, + build_view_model, + discover_runs, + list_episodes, + normalize_task_id, + read_llm_usage, + scrub_paths, +) +from demo.reader.events import noop_stats # noqa: E402 +from demo.reader.llm import read_llm_texts # noqa: E402 + +# ---------------------------------------------------------------- 题号归一 + + +def test_normalize_task_id(): + assert normalize_task_id("tasks-004") == "004" + assert normalize_task_id("004") == "004" + assert normalize_task_id("4") == "004" + assert normalize_task_id("task_013") == "013" + + +# ---------------------------------------------------------------- run / 题发现 + + +def test_discover_runs_reads_metadata(tmp_path): + make_run(tmp_path) + runs = discover_runs(tmp_path, benchmark="osworld21") + assert len(runs) == 1 + run = runs[0] + assert run.run_id == "osw21-run-1" + assert run.benchmark == "osworld21" + assert run.harness["mode"] == "stage" + assert run.code_version == "unknown(experiment)" + assert "sha256" in (run.inner_image or "") + + +def test_list_episodes_normalizes_task_and_keeps_unscored(tmp_path): + run_dir = make_run(tmp_path) + make_episode(run_dir, task_dir="tasks-025", score=None, stop="exception", steps=0) + episodes = list_episodes("osw21-run-1", run_dir / "episodes") + assert [e.task_id for e in episodes] == ["004", "025"] + unscored = episodes[1] + assert unscored.score is None + assert unscored.status == "failed_unscored" + assert unscored.exception_type == "ValueError" + assert episodes[0].native_evaluator is True + + +# ---------------------------------------------------------------- 事件流 + + +def test_build_steps_normalizes_kinds(tmp_path): + episode_dir = make_episode(tmp_path) + steps = build_steps(episode_dir) + kinds = [s["kind"] for s in steps] + assert kinds == ["planner", "gui", "gui", "gui_verified", "stage_replan", "action"] + assert steps[1]["n_candidates"] == 1 + assert steps[1]["ranking"]["latency_ms"] == 42.0 + assert steps[1]["noop"] is False + assert steps[2]["noop"] is True + assert steps[3]["verification"]["outcome"] == "not_achieved" + assert steps[3]["verification"]["requirement_checks"][0]["id"] == "font_name" + assert steps[4]["reason"] == "execution_failed_or_unverified" + + +def test_noop_rate_uses_action_null_caliber(tmp_path): + """空转口径：gui 事件里 action 为 null（与 tools/batch_summary 一致）。""" + episode_dir = make_episode(tmp_path) + stats = noop_stats(episode_dir) + assert stats["decisions"] == 2 + assert stats["noop"] == 1 + assert stats["noop_rate"] == 0.5 + + +# ---------------------------------------------------------------- 慢模型调用 + + +class _NoReadFile: + """只许逐行读的假文件：调用 read()/readlines() 直接炸。""" + + def __init__(self, path: Path, **kwargs): + # 用 io.open 直开，绕开被 monkeypatch 掉的 Path.open（否则自我递归） + self._handle = open(path, encoding="utf-8", errors="replace") + + def __iter__(self): + return self + + def __next__(self): + return next(self._handle) + + def read(self, *args): + raise AssertionError("读取层不允许整文件读 llm_calls.jsonl") + + def readlines(self, *args): + raise AssertionError("读取层不允许整文件读 llm_calls.jsonl") + + def __enter__(self): + return self + + def __exit__(self, *exc): + self._handle.close() + return False + + +def test_llm_reader_never_reads_whole_file(monkeypatch, tmp_path): + episode_dir = make_episode(tmp_path) + original_open = Path.open + + def guarded_open(self, *args, **kwargs): + if self.name == "llm_calls.jsonl": + return _NoReadFile(self) + return original_open(self, *args, **kwargs) + + monkeypatch.setattr(Path, "open", guarded_open) + usage = read_llm_usage(episode_dir) + assert usage["total"]["calls"] == 2 + assert usage["total"]["prompt_tokens"] == 110 + assert usage["total"]["completion_tokens"] == 55 + assert usage["total"]["reasoning_tokens"] == 20 + assert usage["total"]["cached_tokens"] == 7 + + +def test_llm_texts_are_transported_not_generated(tmp_path): + episode_dir = make_episode(tmp_path) + texts = read_llm_texts(episode_dir) + assert texts[0]["text"] == '{"current_goal": "看页脚"}' + assert texts[1]["text"] == "第二段原文" + assert all("truncated" in item for item in texts) + + +# ---------------------------------------------------------------- view model + + +def test_build_view_model_shape(tmp_path): + episode_dir = make_episode(tmp_path) + episodes = list_episodes("osw21-run-1", episode_dir.parent) + model = build_view_model(episodes[0], price={"input_per_1m": 0.15, "output_per_1m": 0.6, + "cached_per_1m": 0.015, "snapshot": "2026-10-23"}) + assert model["task_id"] == "004" + assert model["score"] == 0.6556 + assert model["evaluator"]["native"] is True + assert model["tokens"]["prompt"] == 110 + assert model["tokens"]["usd_estimate"] > 0 + assert model["tokens"]["snapshot"] == "2026-10-23" + assert model["noop_rate"] == 0.5 + assert model["fast_model"]["rank_calls"] == 1 # 没有 ranking 的决策不算排序调用 + assert model["replans"][0]["reason"] == "execution_failed_or_unverified" + assert model["requirements"][0]["id"] == "font_name" + assert model["frames"][0]["node_count"] == 4948 + # 集群路径必须已脱敏 + assert "/mnt/shared-storage-user" not in json.dumps(model, ensure_ascii=False) + + +def test_build_view_model_without_price_has_no_usd(tmp_path): + """没有价格表就不出美元数（红线 3）。""" + episode_dir = make_episode(tmp_path) + episodes = list_episodes("osw21-run-1", episode_dir.parent) + model = build_view_model(episodes[0], price=None) + assert model["tokens"]["usd_estimate"] is None + assert model["tokens"]["snapshot"] is None + + +def test_scrub_paths_keeps_vm_paths(): + value = {"p": "/mnt/shared-storage-user/chenxinquan/x/episode.json", + "vm": "/home/user/Desktop/a.pptx"} + scrubbed = scrub_paths(value) + assert scrubbed["p"].startswith("/") + assert scrubbed["vm"] == "/home/user/Desktop/a.pptx" + + +@pytest.mark.parametrize("raw", ["tasks-004", "004"]) +def test_episode_dir_variants_both_normalize(tmp_path, raw): + episode_dir = make_episode(tmp_path, task_dir=raw) + episodes = list_episodes("run", episode_dir.parent) + assert [e.task_id for e in episodes] == ["004"] From 6e66cb376f82798a9a1471c07a6e003bf7ee3409 Mon Sep 17 00:00:00 2001 From: tandem-demo Date: Mon, 26 Oct 2026 14:58:30 +0800 Subject: [PATCH 04/31] =?UTF-8?q?feat(demo):=20=E8=81=9A=E5=90=88=E5=B1=82?= =?UTF-8?q?=E5=A7=94=E6=89=98=20tools/batch=5Fsummary=20+=20Sai=20?= =?UTF-8?q?=E9=80=90=E9=A2=98=E5=AF=B9=E7=85=A7=E5=88=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 站点的逐题数字**不自己实现**：直接委托 tools/batch_summary.collect_rows （团队已认可的聚合实现，空转口径 10-25 修正过），demo 只加题号归一与 Sai 对照列。一致性测试守着「demo == tools」这条线。 tools/batch_summary._episode_tokens 顺带补出 cached_tokens（成本轴要用， 缓存命中价差 10 倍，不能并进普通输入价）。 --- demo/reader/__init__.py | 5 + demo/reader/aggregate.py | 220 +++++++++++++++++++++++++++++++++++ tests/demo/test_aggregate.py | 133 +++++++++++++++++++++ tools/batch_summary.py | 15 ++- 4 files changed, 367 insertions(+), 6 deletions(-) create mode 100644 demo/reader/aggregate.py create mode 100644 tests/demo/test_aggregate.py diff --git a/demo/reader/__init__.py b/demo/reader/__init__.py index d117d33..9b3f529 100644 --- a/demo/reader/__init__.py +++ b/demo/reader/__init__.py @@ -21,6 +21,7 @@ from __future__ import annotations +from .aggregate import build_overview, collect_rows, compare_rows, load_official from .episodes import EpisodeRef, list_episodes, read_episode_meta from .events import build_steps, noop_stats, rank_stats, read_events from .llm import read_llm_texts, read_llm_usage @@ -32,10 +33,14 @@ __all__ = [ "EpisodeRef", "RunInfo", + "build_overview", "build_steps", "build_view_model", + "collect_rows", + "compare_rows", "discover_runs", "list_episodes", + "load_official", "noop_stats", "normalize_task_id", "rank_stats", diff --git a/demo/reader/aggregate.py b/demo/reader/aggregate.py new file mode 100644 index 0000000..0f31a98 --- /dev/null +++ b/demo/reader/aggregate.py @@ -0,0 +1,220 @@ +"""逐题行 → 站点的总览与对照数据。 + +**唯一实现原则**：逐题行直接委托 `tools/batch_summary.py`（团队已认可的聚合实现， +空转口径 2026-10-25 修正过）。demo 侧**只加两样东西**： + +1. **题号归一**（`tasks-004` / `004` → `004`），否则跨批次会把同一题当两道； +2. **Sai 对照列**（同 release、同原生评测器的官方 harness 逐题分）。 + +这样站点的数字与 `tools/fill_baseline.py` 生成的报告**逐位一致**—— +一致性测试（`tests/demo/test_aggregate.py`）守着这条线。 +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path +from typing import Any + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools.batch_summary import collect_rows as _collect_rows # noqa: E402 +from tools.batch_summary import summarize as _summarize # noqa: E402 + +from .paths import normalize_task_id # noqa: E402 + +#: 官方榜里作为背景展示的 e2e 行（v2.1，同步数预算） +BACKGROUND_E2E = ("Claude Opus 5", "GPT-5.6 Sol", "MuseSpark 1.3") + + +def collect_rows(runs_root: Path, prefix: str = "") -> list[dict[str, Any]]: + """逐题行（委托 tools + 题号归一）。""" + rows = _collect_rows(Path(runs_root), prefix) + for row in rows: + row["task"] = normalize_task_id(row.get("task") or "") + return rows + + +def load_json(path: Path) -> Any: + if not path.is_file(): + return None + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return None + + +def load_official(data_dir: Path) -> dict[str, Any]: + """官方数据快照：榜 + Sai 逐题 + 对比子集。""" + leaderboard = load_json(data_dir / "official" / "leaderboard.json") or {} + sai_tasks = load_json(data_dir / "official" / "sai_tasks.json") or {} + subset = load_json(data_dir / "official" / "compare_subset.json") or {} + prices = load_json(data_dir / "prices.json") or {} + return { + "leaderboard": leaderboard, + "sai_tasks": sai_tasks.get("tasks") or {}, + "sai_meta": {k: v for k, v in sai_tasks.items() if k != "tasks"}, + "subset": subset, + "prices": prices, + } + + +def _price_for(prices: dict[str, Any], model_key: str) -> dict[str, Any] | None: + """按角色名取价格条目（`prices.json` 的 `models.`）。""" + models = prices.get("models") or {} + entry = models.get(model_key) + if not isinstance(entry, dict): + return None + return { + "model": entry.get("model") or model_key, + "input_per_1m": entry.get("input_per_1m"), + "output_per_1m": entry.get("output_per_1m"), + "cached_per_1m": entry.get("cached_per_1m") or 0.0, + "snapshot": entry.get("snapshot"), + "source_url": entry.get("source_url"), + } + + +def _official_rows(leaderboard: dict[str, Any], release: str = "v2.1") -> dict[str, list[dict[str, Any]]]: + """从榜快照里挑出 harness 行与 e2e 背景行。""" + harness: list[dict[str, Any]] = [] + background: list[dict[str, Any]] = [] + for row in leaderboard.get("results") or []: + if str(row.get("releaseVersion")) != release: + continue + if str(row.get("datasetScope")) not in {"full", "None"}: + continue + item = { + "model": row.get("model"), + "model_type": row.get("modelType"), + "reasoning": row.get("reasoning"), + "step_budget": row.get("stepBudget"), + "binary_accuracy": row.get("binaryAccuracy"), + "partial_score": row.get("partialScore"), + "cost_per_task_usd": row.get("costPerTaskUsd"), + "estimated_cost_usd": row.get("estimatedCostUsd"), + "output_tokens_per_task": row.get("outputTokensPerTask"), + } + if str(row.get("modelType")) == "agent": + harness.append(item) + elif str(row.get("model")) in BACKGROUND_E2E: + background.append(item) + harness.sort(key=lambda item: -(item["partial_score"] or 0)) + return {"harness": harness, "e2e_background": background} + + +def build_overview( + rows: list[dict[str, Any]], + *, + data_dir: Path, + dataset_size: int = 108, +) -> dict[str, Any]: + """站点总览：我们的成绩 + 官方参照 + 逐题行。""" + official = load_official(data_dir) + prices = official["prices"] + price = _price_for(prices, "slow_model") + stats = _summarize(rows) + scores = [score for score in stats["scores"] if isinstance(score, (int, float))] + + prompt = sum(row.get("prompt_tokens") or 0 for row in rows) + completion = sum(row.get("completion_tokens") or 0 for row in rows) + cached = sum(row.get("cached_tokens") or 0 for row in rows) + total_tokens = prompt + completion + usd = None + if price: + fresh_input = max(prompt - cached, 0) + usd = ( + fresh_input / 1e6 * float(price.get("input_per_1m") or 0.0) + + cached / 1e6 * float(price.get("cached_per_1m") or 0.0) + + completion / 1e6 * float(price.get("output_per_1m") or 0.0) + ) + + attempted = len(rows) + scored = stats["scored"] + return { + "ours": { + "attempted": attempted, + "scored": scored, + "unscored": attempted - scored, + "partial_scored": stats["average_scored"], + "partial_attempted": (sum(scores) / attempted) if attempted else None, + "partial_over_dataset": (sum(scores) / dataset_size) if dataset_size else None, + "binary": stats["full_hits"], + "binary_rate_attempted": (stats["full_hits"] / attempted) if attempted else None, + "full_hits": stats["full_hits"], + "partial_hits": stats["partial_hits"], + "zeros": stats["zeros"], + "calls": stats["calls"], + "prompt_tokens": prompt, + "completion_tokens": completion, + "cached_tokens": cached, + "total_tokens": total_tokens, + "usd_estimate": round(usd, 4) if usd is not None else None, + "usd_per_task": round(usd / attempted, 4) if usd is not None and attempted else None, + "price": price, + "wall_seconds": stats["wall_seconds"], + "stop_reasons": dict(stats["stop_reasons"]), + "env_failed": len(stats["env_failed"]), + "judge_infra": len(stats["judge_infra"]), + "model_failed": len(stats["model_failed"]), + }, + "official": { + "release": "v2.1", + "dataset_size": dataset_size, + "harness": _official_rows(official["leaderboard"])["harness"], + "e2e_background": _official_rows(official["leaderboard"])["e2e_background"], + "snapshot": official["leaderboard"].get("fetched_at"), + "source_url": official["leaderboard"].get("source_url"), + }, + "sai": official["sai_meta"], + "rows": rows, + } + + +def compare_rows( + rows: list[dict[str, Any]], + sai_tasks: dict[str, Any], + subset: dict[str, Any] | None = None, +) -> list[dict[str, Any]]: + """逐题对照：我们 vs Sai（同一题、同一 release、同一原生评测器）。""" + groups = (subset or {}).get("groups") or {} + out: list[dict[str, Any]] = [] + for row in rows: + task = row.get("task") or "" + sai = sai_tasks.get(task) or {} + best = sai.get("best") + mean = sai.get("mean") + score = row.get("score") + delta = None + if isinstance(score, (int, float)) and isinstance(best, (int, float)): + delta = round(best - score, 4) + out.append( + { + "task": task, + "ours": score, + "ours_status": row.get("status"), + "ours_stop": row.get("stop"), + "ours_steps": row.get("steps"), + "ours_wall": row.get("wall_seconds"), + "ours_noop_rate": row.get("noop_rate"), + "ours_tokens": (row.get("prompt_tokens") or 0) + (row.get("completion_tokens") or 0), + "sai_best": best, + "sai_mean": mean, + "sai_runs": sai.get("runs"), + "delta": delta, + "tie": delta is not None and abs(delta) < 0.01, + "group": _group_of(groups, task), + } + ) + out.sort(key=lambda item: item["task"]) + return out + + +def _group_of(groups: dict[str, Any], task: str) -> str: + for name, tasks in groups.items(): + if task in (tasks or []): + return name + return "" diff --git a/tests/demo/test_aggregate.py b/tests/demo/test_aggregate.py new file mode 100644 index 0000000..36f74a4 --- /dev/null +++ b/tests/demo/test_aggregate.py @@ -0,0 +1,133 @@ +"""demo 聚合层单测：**数字必须与 `tools/batch_summary.py` 同源**。 + +这是整个 demo 最重要的一条测试：站点显示的每一批数字都由 `tools/batch_summary.py` +算出来，demo 只做「题号归一 + Sai 对照列」。如果两边分叉，站点就会开始说谎。 +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) +if str(REPO_ROOT / "tools") not in sys.path: + sys.path.insert(0, str(REPO_ROOT / "tools")) + +from helpers import make_contract_episode as make_episode # noqa: E402 +from helpers import make_contract_run as make_run # noqa: E402 + +from demo.reader.aggregate import ( # noqa: E402 + build_overview, + collect_rows, + compare_rows, + load_official, +) +from tools.batch_summary import collect_rows as tools_collect_rows # noqa: E402 +from tools.batch_summary import summarize as tools_summarize # noqa: E402 + +DATA_DIR = REPO_ROOT / "demo" / "data" + + +def test_demo_rows_match_tools_rows(tmp_path): + """同一个 runs-root：demo 的逐题行与 tools 的逐题行逐位一致（题号归一除外）。""" + run_dir = make_run(tmp_path) + make_episode(run_dir, task_dir="tasks-025", score=None, stop="exception", steps=0) + demo_rows = collect_rows(tmp_path) + tool_rows = tools_collect_rows(tmp_path) + assert len(demo_rows) == len(tool_rows) == 2 + for demo_row, tool_row in zip(demo_rows, tool_rows, strict=True): + assert demo_row["score"] == tool_row["score"] + assert demo_row["status"] == tool_row["status"] + assert demo_row["stop"] == tool_row["stop"] + assert demo_row["steps"] == tool_row["steps"] + assert demo_row["calls"] == tool_row["calls"] + assert demo_row["prompt_tokens"] == tool_row["prompt_tokens"] + assert demo_row["completion_tokens"] == tool_row["completion_tokens"] + assert demo_row["cached_tokens"] == tool_row["cached_tokens"] + assert demo_row["noop_rate"] == tool_row["noop_rate"] + assert {row["task"] for row in demo_rows} == {"004", "025"} + + +def test_overview_numbers_match_tools_summary(tmp_path): + make_run(tmp_path) + rows = collect_rows(tmp_path) + stats = tools_summarize(rows) + overview = build_overview(rows, data_dir=DATA_DIR) + ours = overview["ours"] + assert ours["attempted"] == stats["total"] + assert ours["scored"] == stats["scored"] + assert ours["partial_scored"] == stats["average_scored"] + assert ours["partial_attempted"] == stats["average_all"] + assert ours["binary"] == stats["full_hits"] + assert ours["prompt_tokens"] == stats["prompt_tokens"] + assert ours["completion_tokens"] == stats["completion_tokens"] + assert ours["wall_seconds"] == stats["wall_seconds"] + + +def test_overview_has_no_predicted_scores(tmp_path): + """红线 2：站点不生成我们的预测分/目标分。""" + make_run(tmp_path) + overview = build_overview(collect_rows(tmp_path), data_dir=DATA_DIR) + blob = json.dumps(overview, ensure_ascii=False).lower() + for forbidden in ("predicted", "forecast", "target_score", "预期分", "预计分"): + assert forbidden not in blob + + +def test_sai_anchor_matches_leaderboard(): + """口径锚点：Sai per-task best 均值必须等于官方榜上那行的 partial。 + + 官方榜 `Sai w/ Claude Opus 5`（v2.1）的 partial 79.38 = HF 参考 run + 「每题最佳干净运行」的均值 0.7938 —— 对不上就说明 vendored 数据串了。 + """ + official = load_official(DATA_DIR) + sai = official["sai_meta"] + assert sai["tasks_total"] == 108 + assert sai["headline"]["mean_of_task_best"] == 0.7938 + sai_rows = [ + row for row in official["leaderboard"]["results"] + if str(row.get("modelType")) == "agent" and str(row.get("releaseVersion")) == "v2.1" + and "Sai" in str(row.get("model")) + ] + assert len(sai_rows) == 1 + assert round(sai_rows[0]["partialScore"] / 100, 4) == sai["headline"]["mean_of_task_best"] + + +def test_official_harness_rows_are_exposed(): + overview = build_overview([], data_dir=DATA_DIR) + harness = overview["official"]["harness"] + assert any("Sai" in row["model"] for row in harness) + sai = next(row for row in harness if "Sai" in row["model"]) + assert sai["binary_accuracy"] == 41.67 + assert sai["partial_score"] == 79.38 + assert sai["cost_per_task_usd"] == 14.34 + # e2e 行只作背景 + assert all(row["model_type"] == "e2e" for row in overview["official"]["e2e_background"]) + + +def test_compare_rows_delta_and_tie(tmp_path): + make_run(tmp_path) + rows = collect_rows(tmp_path) + sai_tasks = {"004": {"best": 0.8, "mean": 0.8, "runs": 1}} + subset = {"groups": {"接近但落后": ["004"]}} + compared = compare_rows(rows, sai_tasks, subset) + row = compared[0] + assert row["task"] == "004" + assert row["ours"] == 0.6556 + assert row["sai_best"] == 0.8 + assert row["delta"] == 0.1444 + assert row["tie"] is False + assert row["group"] == "接近但落后" + + +def test_compare_rows_marks_tie_and_missing(tmp_path): + make_run(tmp_path) + rows = collect_rows(tmp_path) + sai_tasks = {"004": {"best": 0.6556, "mean": 0.6556, "runs": 1}} + compared = compare_rows(rows, sai_tasks) + assert compared[0]["tie"] is True + assert compared[0]["delta"] == 0.0 + # Sai 没跑的题 → delta 为 None（不折算 0） + assert compare_rows(rows, {})[0]["delta"] is None diff --git a/tools/batch_summary.py b/tools/batch_summary.py index 2034c99..39b646c 100644 --- a/tools/batch_summary.py +++ b/tools/batch_summary.py @@ -135,12 +135,12 @@ def _steps_from_events(episode_dir: Path) -> int: return last -def _episode_tokens(episode_dir: Path) -> tuple[int, int, int]: - """返回 (调用次数, prompt tokens, completion tokens)；只统计慢模型调用。""" - calls = prompt = completion = 0 +def _episode_tokens(episode_dir: Path) -> tuple[int, int, int, int]: + """返回 (调用次数, prompt tokens, completion tokens, cached tokens)；只统计慢模型调用。""" + calls = prompt = completion = cached = 0 llm_calls = episode_dir / "llm_calls.jsonl" if not llm_calls.is_file(): - return 0, 0, 0 + return 0, 0, 0, 0 for line in llm_calls.read_text(encoding="utf-8").splitlines(): record = _read_json_line(line) if not record: @@ -149,7 +149,9 @@ def _episode_tokens(episode_dir: Path) -> tuple[int, int, int]: calls += 1 prompt += usage.get("prompt_tokens") or 0 completion += usage.get("completion_tokens") or 0 - return calls, prompt, completion + details = usage.get("prompt_tokens_details") or {} + cached += details.get("cached_tokens") or 0 + return calls, prompt, completion, cached def _read_json_line(line: str) -> dict[str, Any]: @@ -180,7 +182,7 @@ def collect_rows(runs_root: Path, prefix: str = "") -> list[dict[str, Any]]: if not episode: continue timing = _read_json(episode_dir / "timing.json") - calls, prompt, completion = _episode_tokens(episode_dir) + calls, prompt, completion, cached = _episode_tokens(episode_dir) steps = episode.get("steps") if not isinstance(steps, int) or steps <= 0: # 异常路径下 episode.json 的 steps 记成 0，用事件流回补 @@ -203,6 +205,7 @@ def collect_rows(runs_root: Path, prefix: str = "") -> list[dict[str, Any]]: "calls": calls, "prompt_tokens": prompt, "completion_tokens": completion, + "cached_tokens": cached, }) best: dict[str, dict[str, Any]] = {} for row in rows: From 53a7fe3dad3e170f14028a66edf300a5a2a09fdb Mon Sep 17 00:00:00 2001 From: tandem-demo Date: Mon, 26 Oct 2026 14:58:30 +0800 Subject: [PATCH 05/31] =?UTF-8?q?feat(demo):=20=E5=AE=98=E6=96=B9=E6=A6=9C?= =?UTF-8?q?/Sai=20=E9=80=90=E9=A2=98=E5=88=86=E5=BF=AB=E7=85=A7=20+=20?= =?UTF-8?q?=E4=BB=B7=E6=A0=BC=E8=A1=A8=20+=20=E5=88=B7=E6=96=B0=E8=84=9A?= =?UTF-8?q?=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 站点离线可开，官方数据必须 vendored： - official/leaderboard.json：官方榜 55 行快照（含 source_url/fetched_at） - official/sai_tasks.json：Sai 逐题 mean/first/best（HF 134 次干净运行） - official/sai_traj/：对比子集 12 题的 Sai 轨迹摘要 - prices.json：挂牌价（带来源与快照日期，脚本查新鲜度） - sync_official.py：刷新脚本（下载 → 校验 → 写回） 口径锚点：Sai per-task best 均值 0.7938 == 官方榜 Sai 行 partial 79.38。 --- demo/data/official/compare_subset.json | 15 + demo/data/official/leaderboard.json | 773 +++++++++ demo/data/official/sai_tasks.json | 1203 ++++++++++++++ demo/data/official/sai_traj/001.json | 828 ++++++++++ demo/data/official/sai_traj/003.json | 1023 ++++++++++++ demo/data/official/sai_traj/004.json | 723 +++++++++ demo/data/official/sai_traj/013.json | 1248 ++++++++++++++ demo/data/official/sai_traj/023.json | 888 ++++++++++ demo/data/official/sai_traj/026.json | 2058 ++++++++++++++++++++++++ demo/data/official/sai_traj/027.json | 453 ++++++ demo/data/official/sai_traj/034.json | 1572 ++++++++++++++++++ demo/data/official/sai_traj/035.json | 1053 ++++++++++++ demo/data/official/sai_traj/054.json | 948 +++++++++++ demo/data/official/sai_traj/061.json | 453 ++++++ demo/data/official/sai_traj/089.json | 1248 ++++++++++++++ demo/data/prices.json | 33 + demo/sync_official.py | 255 +++ 17 files changed, 14774 insertions(+) create mode 100644 demo/data/official/compare_subset.json create mode 100644 demo/data/official/leaderboard.json create mode 100644 demo/data/official/sai_tasks.json create mode 100644 demo/data/official/sai_traj/001.json create mode 100644 demo/data/official/sai_traj/003.json create mode 100644 demo/data/official/sai_traj/004.json create mode 100644 demo/data/official/sai_traj/013.json create mode 100644 demo/data/official/sai_traj/023.json create mode 100644 demo/data/official/sai_traj/026.json create mode 100644 demo/data/official/sai_traj/027.json create mode 100644 demo/data/official/sai_traj/034.json create mode 100644 demo/data/official/sai_traj/035.json create mode 100644 demo/data/official/sai_traj/054.json create mode 100644 demo/data/official/sai_traj/061.json create mode 100644 demo/data/official/sai_traj/089.json create mode 100644 demo/data/prices.json create mode 100644 demo/sync_official.py diff --git a/demo/data/official/compare_subset.json b/demo/data/official/compare_subset.json new file mode 100644 index 0000000..7a59ffa --- /dev/null +++ b/demo/data/official/compare_subset.json @@ -0,0 +1,15 @@ +{ + "note": "MVP 对比子集：人工挑选 12 题，覆盖 4 种模式（满分打平 / 同分非满分 / 接近但落后 / 失败模式），全部落在我们已出分的题里，便于逐题对照官方 Sai。", + "groups": { + "满分打平": ["001", "013", "027", "061"], + "同分非满分": ["023", "026", "035"], + "接近但落后": ["004", "054", "089"], + "失败模式": ["003", "034"] + }, + "groups_note": { + "满分打平": "我们与 Sai 的最佳干净运行同为 1.00", + "同分非满分": "逐位相同的部分分（0.93 / 0.50 / 0.50）", + "接近但落后": "差距不大，差在哪要靠轨迹对照看", + "失败模式": "003 声称 done 却 0 分（Sai 1.0）；034 空转过多触发 no_action_limit（Sai 0.875）" + } +} diff --git a/demo/data/official/leaderboard.json b/demo/data/official/leaderboard.json new file mode 100644 index 0000000..f667313 --- /dev/null +++ b/demo/data/official/leaderboard.json @@ -0,0 +1,773 @@ +{ + "source_url": "https://osworld-v2.xlang.ai/static/data/leaderboard/official-results.json", + "fetched_at": "2026-10-26T06:55:09Z", + "dataset_size": 108, + "default_step_budget": 500, + "release_versions": [ + "v2026.06.24", + "v2026.08.08", + "v2.1" + ], + "metrics": { + "binaryAccuracy": "Binary Accuracy", + "partialScore": "Partial Score" + }, + "results": [ + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "low", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "full", + "binaryAccuracy": 18.81, + "partialScore": 59.09, + "outputTokensPerTask": 29242, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "medium", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "full", + "binaryAccuracy": 33.01, + "partialScore": 64.85, + "outputTokensPerTask": 48645, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "high", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "full", + "binaryAccuracy": 36.89, + "partialScore": 68.92, + "outputTokensPerTask": 65555, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "xhigh", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "full", + "binaryAccuracy": 33.33, + "partialScore": 73.11, + "outputTokensPerTask": 88387, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "full", + "binaryAccuracy": 44.33, + "partialScore": 77.67, + "outputTokensPerTask": 107919, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "low", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "offline", + "binaryAccuracy": 22.08, + "partialScore": 60.34, + "outputTokensPerTask": 28902, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "medium", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "offline", + "binaryAccuracy": 37.18, + "partialScore": 66.25, + "outputTokensPerTask": 51770, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "high", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "offline", + "binaryAccuracy": 39.74, + "partialScore": 70.55, + "outputTokensPerTask": 63098, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "xhigh", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "offline", + "binaryAccuracy": 34.67, + "partialScore": 72.03, + "outputTokensPerTask": 88228, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2.1", + "datasetScope": "offline", + "binaryAccuracy": 48.65, + "partialScore": 79.2, + "outputTokensPerTask": 113319, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "low", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 22.32, + "partialScore": 54.89, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "medium", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 25.33, + "partialScore": 60.49, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "high", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 28.98, + "partialScore": 63.79, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "xhigh", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 30.21, + "partialScore": 67.67, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 31.43, + "partialScore": 68.31, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "full", + "binaryAccuracy": 27.34, + "partialScore": 62.72, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "low", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 24.61, + "partialScore": 55.22, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "medium", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 27.13, + "partialScore": 60.26, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "high", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 32.69, + "partialScore": 65.92, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "xhigh", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 33.38, + "partialScore": 70.14, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 5", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 34.72, + "partialScore": 70.19, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "none", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 3.41, + "partialScore": 16.43, + "estimatedCostUsd": 96.12, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "low", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 7.32, + "partialScore": 29.63, + "estimatedCostUsd": 189, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "medium", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 17.56, + "partialScore": 49.01, + "estimatedCostUsd": 379.08, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "high", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 20, + "partialScore": 56.03, + "estimatedCostUsd": 679.32, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "xhigh", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 24.63, + "partialScore": 60.6, + "estimatedCostUsd": 849.96, + "official": true, + "modelType": "e2e" + }, + { + "model": "GPT-5.6 Sol", + "modelFamily": "GPT", + "reasoning": "max", + "toolSetting": "batch tool", + "stepBudget": 500, + "releaseVersion": "v2026.08.08", + "datasetScope": "offline", + "binaryAccuracy": 28.1, + "partialScore": 64.13, + "estimatedCostUsd": 1116.72, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.8", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batched tool", + "stepBudget": 500, + "binaryAccuracy": 20.6, + "partialScore": 54.8, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.8", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "standard", + "stepBudget": 500, + "binaryAccuracy": 18.52, + "partialScore": 49.33, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.7", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "batched tool", + "stepBudget": 500, + "binaryAccuracy": 18.2, + "partialScore": 48.91, + "estimatedCostUsd": null, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.7", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "standard", + "stepBudget": 150, + "binaryAccuracy": 4.6, + "partialScore": 20.3, + "estimatedCostUsd": 1030, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.7", + "modelFamily": "Claude", + "reasoning": "max", + "toolSetting": "standard", + "stepBudget": 300, + "binaryAccuracy": 13.0, + "partialScore": 39.8, + "estimatedCostUsd": 2470, + "official": true, + "modelType": "e2e" + }, + { + "model": "Claude Opus 4.7", + "modelFamily": 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"v21-ref7-20260923T130535Z", + "score": 1.0, + "mean": 1.0, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref7-20260923T130535Z/001/traj.jsonl", + "fetched_at": "2026-10-26T06:55:12Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01SNcRgAb3xdQohKpomsgub9", + "name": "execute", + "input": { + "title": "Checking open windows on the desktop", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar wins = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins, null, 2))" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@130739845665.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01C5FgoNzdtuxEhNWYFT41BA", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"wmctrl -l -p 2>/dev/null || xdotool search --name '' getwindowname %@ 2>/dev/null | head -50\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Listing desktop windows via shell" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@130803367803.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01DfM3HaHk2EKHNfU6MpnPRo", + "name": "execute", + "input": { + "code": "var app = await machine.getAppWindow(1)\nawait app.screenshot()", + "title": "Taking a screenshot of the desktop" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@130835696304.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01NivjD18wJTueR6ZgkXLZHf", + "name": "execute", + "input": { + "code": "var r1 = await machine.exec({ command: \"ls -la ~/ && echo '---' && ls -la ~/.thunderbird 2>/dev/null && echo '---' && ls -la ~/.local/share/evolution 2>/dev/null\" })\nconsole.log(r1.stdout, r1.stderr)", + "title": "Exploring home directory, Thunderbird and calendar data" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@130903357794.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_01HwveKiJeLE23W7jf7tACLM", + "name": "execute", + "input": { + "code": "var r2 = await machine.exec({ command: \"ls -laR ~/.local/share/evolution/calendar/ && echo '=== MAIL ===' && find ~/.thunderbird/wtkk3c2w.default-release -maxdepth 3 -name '*' -type d | head -50\" })\nconsole.log(r2.stdout, r2.stderr)", + "title": "Inspecting calendar store and Thunderbird profile" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@130927044941.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_017qN3wf5x8uV8zweEZqk5M7", + "name": "execute", + "input": { + "code": "var cal = await machine.readFile({ path: \"/home/user/.local/share/evolution/calendar/system/calendar.ics\" })\nconsole.log(cal)", + "title": "Reading the existing Ubuntu calendar events" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@130948757621.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_014A11QNDhWfNV5qg4AHVS8G", + "name": "execute", + "input": { + "code": "var r3 = await machine.exec({ command: \"find ~/.thunderbird/wtkk3c2w.default-release/Mail ~/.thunderbird/wtkk3c2w.default-release/ImapMail -type f 2>/dev/null | head -60; echo '=== sizes ==='; find ~/.thunderbird -type f -size +2k \\\\( -name 'Inbox' -o -name '*.msf' -o -name 'INBOX' \\\\) -exec ls -la {} \\\\; 2>/dev/null | head -40\" })\nconsole.log(r3.stdout, r3.stderr)", + "title": "Locating the Thunderbird mail files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@131015151293.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01NSLVK2eYv7onY6iAU2xTg5", + "name": "execute", + "input": { + "code": "var r4 = await machine.exec({ command: \"grep -n '^Subject:\\\\|^From:\\\\|^Date:' '/home/user/.thunderbird/wtkk3c2w.default-release/Mail/outlook.office365.com/Inbox' | head -100\" })\nconsole.log(r4.stdout, r4.stderr)", + "title": "Listing email subjects in the inbox" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@131037656900.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_01DaJnQLZRqDjSR1ze2z8ib3", + "name": "execute", + "input": { + "code": "var r5 = await machine.exec({ command: \"sed -n '1840,2400p' '/home/user/.thunderbird/wtkk3c2w.default-release/Mail/outlook.office365.com/Inbox' > /tmp/mail_raw.txt; wc -l /tmp/mail_raw.txt; head -60 /tmp/mail_raw.txt\" })\nconsole.log(r5.stdout, r5.stderr)", + "title": "Extracting the FYP schedule email" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@131101315479.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_016BL9qT1WjgLgTxdoGYwMnt", + "name": "execute", + "input": { + "code": "var py = `\nimport mailbox, email, os, base64, quopri\np = '/home/user/.thunderbird/wtkk3c2w.default-release/Mail/outlook.office365.com/Inbox'\nmb = mailbox.mbox(p)\nfor i, msg in enumerate(mb):\n subj = msg.get('Subject','')\n if 'FYP' in subj or '7203' in subj:\n print('IDX', i, '|', subj)\n print('FROM', msg.get('From'))\n for part in msg.walk():\n ct = part.get_content_type()\n fn = part.get_filename()\n print(' PART', ct, 'filename=', fn, 'size=', len(part.get_payload(decode=True) or b''))\n`;\nawait machine.writeToFile({ text: py, path: \"/tmp/scan.py\", overwrite: true })\nvar r6 = await machine.exec({ command: \"python3 /tmp/scan.py\" })\nconsole.log(r6.stdout, r6.stderr)", + "title": "Parsing the email structure and attachments" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@131126057773.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01RANayFPV57CHfs3NfVxdaK", + "name": "execute", + "input": { + "code": "var py2 = `\nimport mailbox\np = '/home/user/.thunderbird/wtkk3c2w.default-release/Mail/outlook.office365.com/Inbox'\nmb = mailbox.mbox(p)\nmsg = list(mb)[2]\nfor part in msg.walk():\n if part.get_content_type() == 'text/plain':\n print(part.get_payload(decode=True).decode('utf-8', 'replace'))\n break\nfor part in msg.walk():\n fn = part.get_filename()\n if fn:\n open('/tmp/sched.xlsx','wb').write(part.get_payload(decode=True))\n print('SAVED', fn)\n`;\nawait machine.writeToFile({ text: py2, path: \"/tmp/scan2.py\", overwrite: true })\nvar r7 = await machine.exec({ command: \"python3 /tmp/scan2.py\" })\nconsole.log(r7.stdout, r7.stderr)", + "title": "Reading the email body and saving the attachment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@131151571578.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_014tfZPc4jtLzcGGaHHEufPt", + "name": "execute", + "input": { + "code": "var r8 = await machine.exec({ command: \"python3 -c \\\"import openpyxl; print('ok')\\\" 2>&1; pip3 list 2>/dev/null | grep -i openpyxl\" })\nconsole.log(r8.stdout, r8.stderr)", + "title": "Checking spreadsheet library availability" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@131217859134.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01BtmDYMembZnEnHRi7VCemP", + "name": "execute", + "input": { + "code": "var py3 = `\nimport mailbox\np = '/home/user/.thunderbird/wtkk3c2w.default-release/Mail/outlook.office365.com/Inbox'\nmb = mailbox.mbox(p)\nmsg = list(mb)[2]\nfor part in msg.walk():\n if part.get_content_type() == 'text/plain':\n open('/tmp/body.txt','w').write(part.get_payload(decode=True).decode('utf-8','replace'))\n break\n`;\nawait machine.writeToFile({ text: py3, path: \"/tmp/scan3.py\", overwrite: true })\nawait machine.exec({ command: \"python3 /tmp/scan3.py\" })\nvar body = await machine.readFile({ path: \"/tmp/body.txt\" })\nconsole.log(\"=====BODY=====\")\nconsole.log(body)", + "title": "Reading the full email body text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@131242683715.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01X5chZaYcX3Z9ACz1kJzm4Q", + "name": "execute", + "input": { + "code": "var pyx = `\nimport zipfile, re\nfrom xml.etree import ElementTree as ET\nNS={'m':'http://schemas.openxmlformats.org/spreadsheetml/2006/main',\n 'r':'http://schemas.openxmlformats.org/officeDocument/2006/relationships'}\nz=zipfile.ZipFile('/tmp/sched.xlsx')\nprint(z.namelist())\n# shared strings\nsst=[]\ntry:\n root=ET.fromstring(z.read('xl/sharedStrings.xml'))\n for si in root.findall('m:si',NS):\n sst.append(''.join(t.text or '' for t in si.iter('{http://schemas.openxmlformats.org/spreadsheetml/2006/main}t')))\nexcept KeyError:\n pass\nprint('SST count', len(sst))\nwb=ET.fromstring(z.read('xl/workbook.xml'))\nfor sh in wb.find('m:sheets',NS):\n print('SHEET', sh.get('name'), sh.get('sheetId'), sh.get('{http://schemas.openxmlformats.org/officeDocument/2006/relationships}id'))\n`;\nawait machine.writeToFile({ text: pyx, path: \"/tmp/x1.py\", overwrite: true })\nvar r9 = await machine.exec({ command: \"python3 /tmp/x1.py\" })\nconsole.log(r9.stdout, r9.stderr)", + "title": "Opening the schedule spreadsheet" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@131309857838.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_0127dRtQ5tC3ymLxVkyFWdTr", + "name": "execute", + "input": { + "code": "console.log(\"LEN\", body.length)\nconsole.log(body.slice(0, 700))", + "title": "Reading first half of the email body" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@131333903043.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01CWkWqHz5QaJWLsB6qswNMn", + "name": "execute", + "input": { + "code": "console.log(body.slice(690))", + "title": "Reading the rest of the email body" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@131354629980.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01LaqCBfF1gZncQGfrCMMU3Q", + "name": "execute", + "input": { + "code": "console.log(body.slice(180, 700))", + "title": "Reading the middle section of the email body" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@131416072754.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01XwzZhAvjNLGamaa18fu1Cu", + "name": "execute", + "input": { + "code": "var pyd = `\nimport zipfile, re, datetime\nfrom xml.etree import ElementTree as ET\nM='{http://schemas.openxmlformats.org/spreadsheetml/2006/main}'\nR='{http://schemas.openxmlformats.org/officeDocument/2006/relationships}'\nz=zipfile.ZipFile('/tmp/sched.xlsx')\nsst=[]\nroot=ET.fromstring(z.read('xl/sharedStrings.xml'))\nfor si in root:\n sst.append(''.join(t.text or '' for t in si.iter(M+'t')))\nrels={}\nrr=ET.fromstring(z.read('xl/_rels/workbook.xml.rels'))\nfor rel in rr:\n rels[rel.get('Id')]=rel.get('Target')\nwb=ET.fromstring(z.read('xl/workbook.xml'))\nsheets=[]\nfor sh in wb.find(M+'sheets'):\n sheets.append((sh.get('name'), rels[sh.get(R+'id')]))\n\ndef colnum(ref):\n c=re.match(r'([A-Z]+)', ref).group(1)\n n=0\n for ch in c: n=n*26+(ord(ch)-64)\n return n\n\nout=open('/tmp/dump.tsv','w')\nfor name, target in sheets:\n path='xl/'+target if not target.startswith('/') else target[1:]\n ws=ET.fromstring(z.read(path))\n out.write('#### SHEET: '+name+'\\\\n')\n sd=ws.find(M+'sheetData')\n for row in sd:\n rn=row.get('r')\n cells={}\n maxc=0\n for c in row:\n ref=c.get('r'); t=c.get('t')\n v=c.find(M+'v'); isx=c.find(M+'is')\n val=''\n if t=='s' and v is not None:\n val=sst[int(v.text)]\n elif t=='inlineStr' and isx is not None:\n val=''.join(x.text or '' for x in isx.iter(M+'t'))\n elif v is not None:\n val=v.text\n cn=colnum(ref)\n cells[cn]=(val or '').replace('\\\\n',' / ').strip()\n maxc=max(maxc,cn)\n line='\\\\t'.join(cells.get(i,'') for i in range(1,maxc+1))\n if line.strip():\n out.write(rn+'\\\\t'+line+'\\\\n')\n out.write('\\\\n')\nout.close()\nprint(open('/tmp/dump.tsv').read()[:200])\nimport os; print('bytes', os.path.getsize('/tmp/dump.tsv'))\n`;\nawait machine.writeToFile({ text: pyd, path: \"/tmp/dump.py\", overwrite: true })\nvar r10 = await machine.exec({ command: \"python3 /tmp/dump.py\" })\nconsole.log(r10.stdout, r10.stderr)", + "title": "Dumping all spreadsheet sheets to text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@131447788091.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_015bYiJyo8LtFSCE2kJv7Xcr", + "name": "execute", + "input": { + "code": "var r11 = await machine.exec({ command: \"grep -n -i 'adams' /tmp/dump.tsv | head -40\" })\nconsole.log(r11.stdout, r11.stderr)", + "title": "Searching the schedule for Leslie Adams" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@131512841103.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_01Mu19KwZxkJ5zN3Bw2mu69k", + "name": "execute", + "input": { + "code": "var r12 = await machine.exec({ command: \"grep -n '#### SHEET' /tmp/dump.tsv\" })\nconsole.log(r12.stdout)\nvar r13 = await machine.exec({ command: \"sed -n '1,60p' /tmp/dump.tsv | cut -c1-400\" })\nconsole.log(\"=== MASTER (first 60 lines, truncated cols) ===\")\nconsole.log(r13.stdout)", + "title": "Mapping the sheet layout of the schedule" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@131539068808.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_0176x9DGPoHy9hmzhsdcG4rX", + "name": "execute", + "input": { + "code": "var r14 = await machine.exec({ command: \"sed -n '115,165p' /tmp/dump.tsv | cut -c1-500\" })\nconsole.log(r14.stdout)", + "title": "Reviewing the 22 and 23 April schedule sheets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@131611282967.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_01SVS4hEb13SzwZfYNmB2tJy", + "name": "execute", + "input": { + "code": "var pym = `\nimport zipfile, re\nfrom xml.etree import ElementTree as ET\nM='{http://schemas.openxmlformats.org/spreadsheetml/2006/main}'\nR='{http://schemas.openxmlformats.org/officeDocument/2006/relationships}'\nz=zipfile.ZipFile('/tmp/sched.xlsx')\nsst=[]\nroot=ET.fromstring(z.read('xl/sharedStrings.xml'))\nfor si in root:\n sst.append(''.join(t.text or '' for t in si.iter(M+'t')))\nrels={}\nfor rel in ET.fromstring(z.read('xl/_rels/workbook.xml.rels')): rels[rel.get('Id')]=rel.get('Target')\nwb=ET.fromstring(z.read('xl/workbook.xml'))\nsheets=[(sh.get('name'), rels[sh.get(R+'id')]) for sh in wb.find(M+'sheets')]\nname, target = sheets[0]\nws=ET.fromstring(z.read('xl/'+target))\n# merged cells\nmc = ws.find(M+'mergeCells')\nprint('MERGES:', [m.get('ref') for m in mc] if mc is not None else None)\nout=open('/tmp/master.txt','w')\nfor row in ws.find(M+'sheetData'):\n rn=row.get('r'); items=[]\n for c in row:\n ref=c.get('r'); t=c.get('t'); v=c.find(M+'v'); isx=c.find(M+'is')\n val=''\n if t=='s' and v is not None: val=sst[int(v.text)]\n elif t=='inlineStr' and isx is not None: val=''.join(x.text or '' for x in isx.iter(M+'t'))\n elif v is not None: val=v.text\n val=(val or '').strip().replace('\\\\n',' / ')\n if val: items.append(ref+'='+val)\n if items: out.write('R'+rn+': '+' | '.join(items)+'\\\\n')\nout.close()\nprint(open('/tmp/master.txt').read()[:100])\n`;\nawait machine.writeToFile({ text: pym, path: \"/tmp/master.py\", overwrite: true })\nvar rm = await machine.exec({ command: \"python3 /tmp/master.py && wc -l /tmp/master.txt\" })\nconsole.log(rm.stdout, rm.stderr)", + "title": "Extracting the master schedule sheet with cell references" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@131644397464.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_01RcunfMrvQLM7RY5DGrD2W3", + "name": "execute", + "input": { + "code": "var master = await machine.readFile({ path: \"/tmp/master.txt\" })\nvar mlines = master.split(\"\\n\")\nconsole.log(\"lines:\", mlines.length)\nconsole.log(mlines.slice(0,14).join(\"\\n\"))", + "title": "Reading the master schedule header rows" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@131707113137.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01UvSnf3GYRxUdBx3e4iG6MF", + "name": "execute", + "input": { + "code": "mlines.forEach((l,i) => { if (/=4613[0-9]|=461[0-9][0-9]\\b/.test(l) || /Adams/.test(l) || /scheduled/i.test(l)) console.log(i, l.slice(0,600)) })", + "title": "Finding date markers and Leslie Adams rows in the master sheet" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@131735520077.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_01Lo3MEVkJXGBqoWDESKgTaW", + "name": "execute", + "input": { + "code": "var pys = `\nimport zipfile, re, datetime, json\nfrom xml.etree import ElementTree as ET\nM='{http://schemas.openxmlformats.org/spreadsheetml/2006/main}'\nR='{http://schemas.openxmlformats.org/officeDocument/2006/relationships}'\nz=zipfile.ZipFile('/tmp/sched.xlsx')\nsst=[]\nfor si in ET.fromstring(z.read('xl/sharedStrings.xml')):\n sst.append(''.join(t.text or '' for t in si.iter(M+'t')))\nrels={}\nfor rel in ET.fromstring(z.read('xl/_rels/workbook.xml.rels')): rels[rel.get('Id')]=rel.get('Target')\nwb=ET.fromstring(z.read('xl/workbook.xml'))\nsheets=[(sh.get('name'), rels[sh.get(R+'id')]) for sh in wb.find(M+'sheets')]\n\ndef colnum(ref):\n c=re.match(r'([A-Z]+)', ref).group(1); n=0\n for ch in c: n=n*26+(ord(ch)-64)\n return n\ndef colname(n):\n s=''\n while n: n,r=divmod(n-1,26); s=chr(65+r)+s\n return s\n\ndata={}\nfor name,target in sheets:\n ws=ET.fromstring(z.read('xl/'+target))\n rows={}\n for row in ws.find(M+'sheetData'):\n rn=int(row.get('r')); cells={}\n for c in row:\n ref=c.get('r'); t=c.get('t'); v=c.find(M+'v'); isx=c.find(M+'is')\n val=''\n if t=='s' and v is not None: val=sst[int(v.text)]\n elif t=='inlineStr' and isx is not None: val=''.join(x.text or '' for x in isx.iter(M+'t'))\n elif v is not None: val=v.text\n val=(val or '').strip().replace('\\\\n',' / ')\n if val: cells[colnum(ref)]=val\n if cells: rows[rn]=cells\n data[name]=rows\njson.dump({'sheets':[s[0] for s in sheets],'data':{k:{str(r):{str(c):v for c,v in cs.items()} for r,cs in rows.items()} for k,rows in data.items()}}, open('/tmp/all.json','w'))\nprint('done', {k:len(v) for k,v in data.items()})\n`;\nawait machine.writeToFile({ text: pys, path: \"/tmp/allx.py\", overwrite: true })\nvar rs = await machine.exec({ command: \"python3 /tmp/allx.py\" })\nconsole.log(rs.stdout, rs.stderr)", + "title": "Extracting all sheet data into structured form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@131827811010.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_015EVqLJHMbUfn5WoeVEfYsE", + "name": "execute", + "input": { + "code": "var allJson = await machine.readFile({ path: \"/tmp/all.json\" })\nvar ALL = JSON.parse(allJson)\nconsole.log(ALL.sheets)\nfunction colname(n){let s='';while(n){n--;s=String.fromCharCode(65+(n%26))+s;n=Math.floor(n/26);}return s}\nfunction sheetRows(name){ const d=ALL.data[name]; return Object.keys(d).map(Number).sort((a,b)=>a-b).map(r=>({r, cells:d[String(r)]})) }\n// print structural map of MasterSchedule: rows containing headers/dates/'To be scheduled'\nvar MS = 'MasterSchedule (22-26 Apr 2026)'\nvar rowsMS = sheetRows(MS)\nvar summary = rowsMS.map(({r,cells})=>{\n const keys = Object.keys(cells).map(Number).sort((a,b)=>a-b)\n return r+': '+keys.map(k=>colname(k)+'='+cells[String(k)]).join(' | ')\n})\nconsole.log(summary.length)\nconsole.log(summary.slice(0,8).map(s=>s.slice(0,300)).join(\"\\n\"))", + "title": "Loading schedule data for analysis" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@131853057576.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01EVdj3tyM6r3fwqgq5kubsB", + "name": "execute", + "input": { + "code": "function excelDate(serial){\n // Excel epoch 1899-12-30\n const ms = (Number(serial)) * 86400000 + Date.UTC(1899,11,30)\n const d = new Date(ms)\n return d.toISOString().slice(0,10)\n}\nconsole.log([46134,46135,46136,46137,46138,46140,46141,46144].map(s=>s+' -> '+excelDate(s)+' '+new Date(Date.UTC(1899,11,30)+s*86400000).toUTCString().slice(0,3)).join(\"\\n\"))\n\n// Find header rows in MasterSchedule\nrowsMS.forEach(({r,cells})=>{\n const ks = Object.keys(cells).map(Number).sort((a,b)=>a-b)\n const venueCols = ks.filter(k=>cells[String(k)]==='Venue')\n if(venueCols.length) console.log('HEADER row', r, 'venue cols:', venueCols.map(colname).join(','))\n})", + "title": "Decoding dates and locating table headers" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@131920870160.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01SV5tXpnWDXRHr5Dok1vmAt", + "name": "execute", + "input": { + "code": "function parseSheet(name){\n const rows = sheetRows(name)\n let curDate = null, curLabel = null, blocks = [], venues = {}\n const out = []\n for(const {r,cells} of rows){\n const g = (c)=> (cells[String(c)]||'').trim()\n const ks = Object.keys(cells).map(Number).sort((a,b)=>a-b)\n const venueCols = ks.filter(k=>g(k)==='Venue')\n if(venueCols.length){ blocks = venueCols; venues = {}; continue }\n // date / section marker row: single cell in col A (or first col)\n if(ks.length===1){\n const v = g(ks[0])\n if(/^\\d{5}$/.test(v)){ curDate = excelDate(v); curLabel = curDate; continue }\n if(/^\\d{1,2}\\s+\\w+\\s+\\d{4}/.test(v)){ curLabel = v; curDate = v; continue }\n if(/to be scheduled/i.test(v)){ curLabel='TO_BE_SCHEDULED'; curDate=null; continue }\n // e.g. 'Lunch' alone, or notes\n out.push({r, note:v, date:curLabel})\n continue\n }\n if(!blocks.length) { out.push({r, note: ks.map(k=>g(k)).join(' | '), date:curLabel}); continue }\n for(const b of blocks){\n const venue=g(b), time=g(b+1), dur=g(b+2), acct=g(b+3), title=g(b+4), sup=g(b+5), se=g(b+6), stud=g(b+7)\n if(venue) venues[b]=venue\n if(!time && !acct) continue\n if(/^lunch$/i.test(time) || /^lunch$/i.test(venue)) continue\n out.push({r, block:colname(b), date:curLabel, venue: venues[b]||venue, time, dur, acct, title, sup, se, stud})\n }\n }\n return out\n}\nvar parsed = {}\nfor(const s of ALL.sheets) parsed[s] = parseSheet(s)\nfor(const s of ALL.sheets) console.log(s, '->', parsed[s].length, 'entries,', parsed[s].filter(e=>e.acct).length, 'with acct')", + "title": "Parsing all schedule tables" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@131954157439.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01C62fGJCyVb1ryYHYTpYHMi", + "name": "execute", + "input": { + "code": "var adams = []\nfor(const s of ALL.sheets){\n for(const e of parsed[s]){\n if(/Leslie\\s+Adams/i.test((e.sup||'')+'|'+(e.se||''))) adams.push({sheet:s, ...e})\n }\n}\nconsole.log('COUNT', adams.length)\nadams.forEach(e=>console.log(`${e.sheet} | r${e.r} blk${e.block} | date=${e.date} | venue=${e.venue} | time=${e.time} | dur=${e.dur} | ${e.acct} | sup=${e.sup} | 2nd=${e.se} | stud=${e.stud}`))", + "title": "Finding all defenses assigned to Leslie Adams" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@132019212140.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01SatG9yXVMtfD2aJXwKFj2T", + "name": "execute", + "input": { + "code": "// show all \"note\" entries across sheets, and the To-be-scheduled section of master\nfor(const s of ALL.sheets){\n const notes = parsed[s].filter(e=>e.note)\n if(notes.length) console.log(s, JSON.stringify(notes))\n}\nconsole.log('--- MASTER rows 130..150 raw ---')\nrowsMS.filter(x=>x.r>=128).forEach(({r,cells})=>{\n const ks=Object.keys(cells).map(Number).sort((a,b)=>a-b)\n console.log(r+': '+ks.map(k=>colname(k)+'='+cells[String(k)]).join(' | ').slice(0,400))\n})", + "title": "Reviewing notes and the to-be-scheduled section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@132055817246.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01FJc4PZS1wLt1G1eAEutntZ", + "name": "execute", + "input": { + "code": "console.log('--- TO BE SCHEDULED rows >=141 ---')\nrowsMS.filter(x=>x.r>=141).forEach(({r,cells})=>{\n const ks=Object.keys(cells).map(Number).sort((a,b)=>a-b)\n console.log(r+': '+ks.map(k=>colname(k)+'='+cells[String(k)]).join(' | ').slice(0,300))\n})", + "title": "Listing the to-be-scheduled groups" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@132126903032.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01XubnQ2iZxNd7Rckn8uFEEa", + "name": "execute", + "input": { + "code": "// Build index by account across sheets to detect discrepancies / reschedules\nvar byAcct = {}\nfor(const s of ALL.sheets){\n for(const e of parsed[s]){\n if(!e.acct) continue\n const k = e.acct\n byAcct[k] = byAcct[k] || []\n byAcct[k].push({sheet:s, date:e.date, venue:e.venue, time:e.time, dur:e.dur, sup:e.sup, se:e.se, stud:e.stud, r:e.r})\n }\n}\n// normalize date labels\nfunction normDate(d){\n if(!d) return 'TBS'\n if(/^\\d{4}-\\d{2}-\\d{2}$/.test(d)) return d\n const m = d.match(/^(\\d{1,2})\\s+(\\w+)\\s+(\\d{4})/)\n if(m){ const mo={Jan:'01',Feb:'02',Mar:'03',Apr:'04',May:'05',Jun:'06',Jul:'07',Aug:'08',Sep:'09',Oct:'10',Nov:'11',Dec:'12'}[m[2].slice(0,3)]; return `${m[3]}-${mo}-${String(m[1]).padStart(2,'0')}` }\n return d\n}\nvar conflicts = []\nObject.keys(byAcct).forEach(k=>{\n const set = new Set(byAcct[k].map(x=>normDate(x.date)+'|'+x.venue+'|'+x.time))\n if(set.size>1) conflicts.push({acct:k, variants:[...set], rows:byAcct[k]})\n})\nconsole.log('accounts total', Object.keys(byAcct).length)\nconsole.log('accounts with differing date/venue/time across sheets:', conflicts.length)\nconflicts.forEach(c=>console.log(c.acct, JSON.stringify(c.variants), '||', c.rows.map(r=>r.sheet+'#'+r.r).join(', ')))", + "title": "Cross-checking master vs daily sheets for discrepancies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@132200719999.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_01H5RujgWNnDpYU7UTWSmjjH", + "name": "execute", + "input": { + "code": "function rawRows(name, from, to){\n const rows = sheetRows(name).filter(x=>x.r>=from && x.r<=to)\n return rows.map(({r,cells})=>{\n const ks=Object.keys(cells).map(Number).sort((a,b)=>a-b)\n return r+': '+ks.map(k=>colname(k)+'='+cells[String(k)]).join(' | ')\n })\n}\nrawRows(MS,1,17).forEach(l=>console.log(l.slice(0,320)))", + "title": "Examining the raw layout of the 22 April section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@132231375277.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_01MuhHcqpkhUNe9UDbKafxyq", + "name": "execute", + "input": { + "code": "console.log('=== MASTER 23 Apr section (rows 31-39) ===')\nrawRows(MS,31,39).forEach(l=>console.log(l.slice(0,300)))\nconsole.log('=== Schedule (23 Apr 2026) full ===')\nrawRows('Schedule (23 Apr 2026)',1,100).forEach(l=>console.log(l.slice(0,300)))", + "title": "Verifying the 23 April defenses" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@132257197919.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_01YDq7ukS9CRnkC54rRdNLGN", + "name": "execute", + "input": { + "code": "console.log('=== MASTER 24 Apr section (rows 40-61) ===')\nrawRows(MS,40,61).forEach(l=>console.log(l.slice(0,320)))", + "title": "Examining the 24 April master section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@132322676355.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_01XqreRVxQbmdopjeh62zqTC", + "name": "execute", + "input": { + "code": "function parseSheet2(name){\n const rows = sheetRows(name)\n let curLabel = null, blocks = [], venues = {}\n const out = []\n const FIELD = {'Venue':'venue','Time':'time','Date/Time':'time','Date':'date2','Presentation Time':'dur','Account No.':'acct','Project Title':'title','Supervisor(s)':'sup','Supervisor':'sup','Second Examiner':'se','Student Name':'stud'}\n for(const {r,cells} of rows){\n const g = (c)=> (cells[String(c)]||'').trim()\n const ks = Object.keys(cells).map(Number).sort((a,b)=>a-b)\n const venueCols = ks.filter(k=>g(k)==='Venue')\n if(venueCols.length){\n // build blocks: for each venue col, collect following header cells until next venue col or gap\n blocks = venueCols.map((vc,i)=>{\n const end = i+1=vc && kg(k)).join(' | '), date:curLabel}); continue }\n for(const b of blocks){\n const rec = {}\n for(const f in b.map) rec[f] = g(b.map[f])\n if(rec.venue) venues[b.start]=rec.venue\n if(!rec.acct && !rec.time) continue\n if(/^lunch$/i.test(rec.time||'') || /^lunch$/i.test(rec.venue||'')) continue\n out.push({r, block:colname(b.start), date:curLabel, ...rec, venue: rec.venue || venues[b.start] || ''})\n }\n }\n return out\n}\nvar parsed2 = {}\nfor(const s2 of ALL.sheets) parsed2[s2] = parseSheet2(s2)\nvar adams2 = []\nfor(const s2 of ALL.sheets) for(const e2 of parsed2[s2]) if(/Leslie\\s+Adams/i.test([e2.sup,e2.se,e2.title,e2.stud].join('|'))) adams2.push({sheet:s2, ...e2})\nconsole.log('ADAMS matches (any field):', adams2.length)\nadams2.forEach(e2=>console.log(`${e2.sheet}#${e2.r} blk${e2.block} ${e2.date} | ${e2.venue} | ${e2.time} | dur=${e2.dur} | ${e2.acct} | sup=${e2.sup} | 2nd=${e2.se} | stud=${e2.stud}`))", + "title": "Re-parsing all blocks with header-aware column mapping" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@132405028444.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_01RoW3K2NVYpG81f2cFTMcDq", + "name": "execute", + "input": { + "code": "var d24 = ALL.data[MS]\nfor(let r=41;r<=61;r++){\n const c = d24[String(r)]\n if(!c) continue\n const A=c['1']||'', B=c['2']||'', C=c['3']||'', D=c['4']||'', F=c['6']||'', G=c['7']||'', H=c['8']||''\n console.log(`r${r} | A(venue)=\"${A}\" | time=${B} | dur=${C} | ${D} | sup=${F} | 2nd=${G} | stud=${H}`)\n}", + "title": "Confirming venues for the 24 April CB417 block" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@132440421380.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01KqSK7o3QJ5Hp2fEGuFHamR", + "name": "execute", + "input": { + "code": "var s24 = 'Schedule (24 Apr 2026)'\nrawRows(s24,1,20).forEach(l=>console.log(l.slice(0,260)))", + "title": "Cross-checking the 24 April daily sheet" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@132506148592.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_012G7HHLAqaudNvCMGUkQqQS", + "name": "execute", + "input": { + "code": "var rcal = await machine.exec({ command: \"ls -la ~/.local/share/evolution/calendar/system/ ; echo '=== full ics ==='; cat ~/.local/share/evolution/calendar/system/calendar.ics; echo '=== sources ==='; ls ~/.config/evolution/sources/ 2>/dev/null\" })\nconsole.log(rcal.stdout, rcal.stderr)", + "title": "Inspecting calendar storage and existing personal holds" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@132543381103.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_01KTvNJ6FiNpRxoYuUgnKqJw", + "name": "execute", + "input": { + "code": "var PLAN = {\n defenses: [\n { id:'fyp47021', date:'20260423', start:'100000', end:'103000', loc:'CB313' },\n { id:'fyp80214', date:'20260424', start:'113000', end:'115000', loc:'CB417' },\n { id:'fyp24010', date:'20260424', start:'115000', end:'121000', loc:'CB417' },\n { id:'fyp04126', date:'20260424', start:'121000', end:'123000', loc:'CB417' },\n { id:'fyp04472', date:'20260424', start:'123000', end:'125000', loc:'CB417' },\n { id:'fyp42007', date:'20260424', start:'140000', end:'143000', loc:'CB417' },\n { id:'fyp48052', date:'20260424', start:'143000', end:'145000', loc:'CB417' },\n ],\n deleteUIDs: ['personal-hold-dentist-001@example.com','personal-hold-coffee-001@example.com'],\n keep: ['Call landlord 04-24 09:00-09:20','Pick up dry cleaning 04-24 15:30-16:00']\n}\nconsole.log(JSON.stringify(PLAN,null,1))\nvar rp = await machine.exec({ command: \"ps aux | grep -i -E 'evolution|gnome-calendar' | grep -v grep; echo '=== gi ==='; python3 -c \\\"import gi; gi.require_version('ECal','2.0'); from gi.repository import ECal, EDataServer; print('ECal OK')\\\" 2>&1 | tail -3\" })\nconsole.log(rp.stdout, rp.stderr)", + "title": "Recording the plan and checking calendar services" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@132620232036.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_01R8o8W6WNNBaCC6FZoxnP8J", + "name": "execute", + "input": { + "code": "var rls = await machine.exec({ command: \"ls -la ~/.local/share/evolution/calendar/system/; echo '--- all under evolution ---'; find ~/.local/share/evolution -maxdepth 4 -type f | head -30; echo '--- procs ---'; pgrep -a -f evolution\" })\nconsole.log(rls.stdout, rls.stderr)", + "title": "Checking for calendar cache files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@132650329219.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_01Mf2BrHQ84vDPG7pP8zsDJq", + "name": "execute", + "input": { + "code": "console.log(JSON.stringify(cal))", + "title": "Reviewing the exact calendar file format" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@132719419632.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_01Cf4GiSekDWPhVLrCs4rCvJ", + "name": "execute", + "input": { + "code": "// Stop EDS processes so they don't overwrite the file\nvar rk = await machine.exec({ command: \"pkill -f evolution-alarm-notify; pkill -f evolution-calendar-factory; pkill -f gnome-calendar; sleep 1; pgrep -a -f evolution; echo 'killed'\" })\nconsole.log(rk.stdout, rk.stderr)\n\nvar TZ = '/freeassociation.sourceforge.net/Asia/Hong_Kong'\nfunction vevent(ev, stampIdx){\n const stamp = '20260923T140000Z'\n return [\n 'BEGIN:VEVENT',\n `UID:${ev.id}-fyp-defense@example.com`,\n `DTSTAMP:${stamp}`,\n `DTSTART;TZID=${TZ}:`,\n ` ${ev.date}T${ev.start}`,\n `DTEND;TZID=${TZ}:`,\n ` ${ev.date}T${ev.end}`,\n `SUMMARY:${ev.id}`,\n `LOCATION:${ev.loc}`,\n `CREATED:${stamp}`,\n `LAST-MODIFIED:${stamp}`,\n 'END:VEVENT'\n ].join('\\n')\n}\n// keep only non-conflicting personal holds from original\nvar keptBlocks = cal.split('BEGIN:VEVENT').slice(1).map(b=>'BEGIN:VEVENT'+b.split('END:VEVENT')[0]+'END:VEVENT')\n .filter(b => !PLAN.deleteUIDs.some(u=>b.includes(u)))\nconsole.log('kept personal holds:', keptBlocks.length)\nkeptBlocks.forEach(b=>console.log(' ', b.match(/SUMMARY:(.*)/)[1]))\n\nvar newIcs = [\n 'BEGIN:VCALENDAR',\n 'CALSCALE:GREGORIAN',\n 'PRODID:-//Ximian//NONSGML Evolution Calendar//EN',\n 'VERSION:2.0',\n 'X-EVOLUTION-DATA-REVISION:2026-09-23T14:00:00.000000Z(20)',\n ...keptBlocks,\n ...PLAN.defenses.map(vevent),\n 'END:VCALENDAR',\n ''\n].join('\\n')\nawait machine.writeToFile({ text: newIcs, path: \"/home/user/.local/share/evolution/calendar/system/calendar.ics\", overwrite: true })\nvar rv = await machine.exec({ command: \"grep -c 'BEGIN:VEVENT' ~/.local/share/evolution/calendar/system/calendar.ics; grep -E 'SUMMARY|LOCATION|DTSTART|^ 2026' ~/.local/share/evolution/calendar/system/calendar.ics\" })\nconsole.log(rv.stdout, rv.stderr)", + "title": "Writing the updated calendar with the defense events" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@132752646978.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_01BWtW9edEnGb4GK28ty1fL8", + "name": "execute", + "input": { + "code": "var gc = await machine.launch('gnome-calendar')\nawait wait({ waitTime: 4 })\nvar gsnap = await gc.snapshot()\nconsole.log(gsnap.snapshot.outline())", + "title": "Opening GNOME Calendar to verify the events" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@132834899318.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_01CfzNp5zDd9yJDr9Agwz61Y", + "name": "execute", + "input": { + "code": "var rg = await machine.exec({ command: \"which gnome-calendar evolution; ls /usr/share/applications/ | grep -i -E 'calendar|evolution'\" })\nconsole.log(rg.stdout, rg.stderr)", + "title": "Locating the calendar application" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@132858261128.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_01PEiVT7YgcGuuFdkEmFNhJr", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"nohup gnome-calendar > /tmp/gc.log 2>&1 & sleep 6; pgrep -a gnome-calendar; wmctrl -l\" })\nconsole.log($lastExec.stdout, $lastExec.stderr)", + "title": "Launching GNOME Calendar" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@132927403694.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_01BWw2UjJxgG6dkKuVFAAR4E", + "name": "execute", + "input": { + "code": "var gc = await machine.getAppWindow('Calendar')\nvar gsnap = await gc.snapshot()\nconsole.log(gsnap.snapshot.outline())", + "title": "Inspecting the Calendar window" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@132942698442.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": { + "id": "toolu_bdrk_01EhMUgbhDKW7mn2w1FLiQaX", + "name": "execute", + "input": { + "code": "await gc.press({ key: 'escape' })\nawait wait({ waitTime: 1 })\n// navigate back 5 months: Sep 2026 -> Apr 2026\nfor(let i=0;i<5;i++){ await gc.click({ ref: '20' }); await wait({ waitTime: 1 }) }\nawait gc.screenshot()", + "title": "Navigating the calendar to April 2026" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_48_20260923@133003352257.png", + "osworld_action": "WAIT" + }, + { + "step_num": 49, + "action": { + "id": "toolu_bdrk_014u8wYLni3xHt5vButsQofb", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1183, y: 741 })\nawait wait({ waitTime: 2 })\nawait gc.screenshot()", + "title": "Opening the fyp47021 event to check details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_49_20260923@133019930946.png", + "osworld_action": "WAIT" + }, + { + "step_num": 50, + "action": { + "id": "toolu_bdrk_01Qx4AAq6aFS94aiq5rJMo8x", + "name": "execute", + "input": { + "code": "await gc.press({ key: 'escape' })\nawait wait({ waitTime: 1 })\n// Switch to week view around Apr 24 by clicking day 24\nawait machine.click({ x: 1524, y: 721 })\nawait wait({ waitTime: 2 })\nawait gc.screenshot()", + "title": "Viewing the April 24 schedule" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_50_20260923@133039417491.png", + "osworld_action": "WAIT" + }, + { + "step_num": 51, + "action": { + "id": "toolu_bdrk_01FeD9vFZ7ioAJaKprssLVrL", + "name": "execute", + "input": { + "code": "await gc.press({ key: 'escape' })\nawait wait({ waitTime: 1 })\n// Switch to Week view (button at ~x=886,y=49)\nawait machine.click({ x: 886, y: 49 })\nawait wait({ waitTime: 2 })\nawait gc.screenshot()", + "title": "Switching to week view for a clearer check" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_51_20260923@133059534441.png", + "osworld_action": "WAIT" + }, + { + "step_num": 52, + "action": { + "id": "toolu_bdrk_019gJSH9tkCThTgKC7FYhmTh", + "name": "execute", + "input": { + "code": "var fin = await machine.exec({ command: \"grep -c 'BEGIN:VEVENT' ~/.local/share/evolution/calendar/system/calendar.ics; echo '---'; python3 - <<'EOF'\\nimport re\\ns=open('/home/user/.local/share/evolution/calendar/system/calendar.ics').read()\\ns=s.replace('\\\\n ','')\\nfor b in s.split('BEGIN:VEVENT')[1:]:\\n b=b.split('END:VEVENT')[0]\\n g=lambda k: (re.search(k+r'[^:]*:(.*)', b).group(1).strip() if re.search(k+r'[^:]*:(.*)', b) else '')\\n print(g('SUMMARY'), '|', g('DTSTART'), '->', g('DTEND'), '|', g('LOCATION'))\\nEOF\" })\nconsole.log(fin.stdout, fin.stderr)", + "title": "Verifying the final saved calendar state" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_52_20260923@133118586898.png", + "osworld_action": "WAIT" + }, + { + "step_num": 53, + "action": { + "id": "toolu_bdrk_016fzKkCcCikJcvnWr3RYMUM", + "name": "execute", + "input": { + "code": "// 1) all distinct supervisor / 2nd examiner names containing 'Adams'\nvar names = new Set()\nfor(const sn of ALL.sheets) for(const e3 of parsed2[sn]){ if(e3.sup) e3.sup.split('/').forEach(x=>names.add(x.trim())); if(e3.se) e3.se.split('/').forEach(x=>names.add(x.trim())) }\nconsole.log('Faculty names containing \"Adams\":', [...names].filter(n=>/adams/i.test(n)))\nconsole.log('Total distinct faculty:', names.size)\n\n// 2) list all messages in all Thunderbird folders\nvar rmail = await machine.exec({ command: \"for f in $(find ~/.thunderbird -type f \\\\( -name 'Inbox' -o -name 'Sent' -o -name 'Archives' -o -name 'Drafts' -o -name 'Junk' -o -name 'Trash' \\\\) -size +1k); do echo \\\"## $f\\\"; grep -a '^Subject:' \\\"$f\\\"; done\" })\nconsole.log(rmail.stdout, rmail.stderr)", + "title": "Cross-checking faculty name variants and all mail folders" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_53_20260923@133139284180.png", + "osworld_action": "WAIT" + }, + { + "step_num": 54, + "action": { + "id": "toolu_bdrk_01DxMp3eDYCe1eNeY7yBnrrq", + "name": "execute", + "input": { + "code": "await gc.screenshot()", + "title": "Final confirmation of the calendar state" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_54_20260923@133153315862.png", + "osworld_action": "WAIT" + }, + { + "step_num": 55, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_55_20260923@133216537296.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/003.json b/demo/data/official/sai_traj/003.json new file mode 100644 index 0000000..592aa51 --- /dev/null +++ b/demo/data/official/sai_traj/003.json @@ -0,0 +1,1023 @@ +{ + "task": "003", + "run": "v21-ref7-20260923T130535Z", + "score": 1.0, + "mean": 1.0, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref7-20260923T130535Z/003/traj.jsonl", + "fetched_at": "2026-10-26T06:55:13Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01Mu9SZrXbEaZH9enhGTyoe3", + "name": "execute", + "input": { + "title": "Checking the Desktop for the task files", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar r = await machine.exec({ command: \"ls -la ~/Desktop && echo '---' && uname -a && echo '---' && which gimp python3 unzip libreoffice\" })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@130728198269.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01AsCXQ68XRP616qF4aYwdUY", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop && mkdir -p /tmp/work && unzip -o city.zip -d /tmp/work/city > /dev/null && unzip -o filter.zip -d /tmp/work/filter > /dev/null && find /tmp/work -type f | sort && echo '---' && ls -la /tmp/work/city /tmp/work/filter\" })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)", + "title": "Extracting the city and filter archives" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@130739482614.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01MvMqUh8Zvt6ydAcVaUkKS9", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp/work && for f in city/city/picture*; do echo -n \\\"$f : \\\"; python3 -c \\\"from PIL import Image; im=Image.open('$f'); print(im.size, im.mode, im.format)\\\"; done; echo '---'; for f in filter/filter/filter*; do echo -n \\\"$f : \\\"; python3 -c \\\"from PIL import Image; im=Image.open('$f'); print(im.size, im.mode, im.format)\\\"; done\" })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)", + "title": "Reading image dimensions and formats" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@130753077812.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_011bLTnZCSWTuKByABTPedTH", + "name": "execute", + "input": { + "code": "var py = `\nfrom PIL import Image, ImageDraw\nimport glob, os\n\ndef sheet(files, out, cols, tw=520, th=360):\n rows = (len(files)+cols-1)//cols\n canvas = Image.new(\"RGB\", (cols*tw, rows*(th+26)), (30,30,30))\n d = ImageDraw.Draw(canvas)\n for i,f in enumerate(files):\n im = Image.open(f).convert(\"RGB\")\n im.thumbnail((tw-8, th-8))\n x = (i%cols)*tw + (tw-im.width)//2\n y = (i//cols)*(th+26) + 26 + (th-im.height)//2\n canvas.paste(im,(x,y))\n d.text(((i%cols)*tw+6, (i//cols)*(th+26)+6), os.path.basename(f), fill=(255,255,0))\n canvas.save(out)\n print(out, canvas.size)\n\ncf = sorted(glob.glob(\"/tmp/work/city/city/picture*\"), key=lambda p: int(''.join(c for c in os.path.basename(p) if c.isdigit())))\nsheet(cf, \"/tmp/work/cities.png\", 3)\n`;\nawait machine.writeToFile({ text: py, path: \"/tmp/work/sheet.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 sheet.py\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Building a contact sheet of the city photos" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@130808782650.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_01Cg317U4ucDGcwEfjMAiDAZ", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cp /tmp/work/cities.png /root/SimularFiles/cities.png 2>/dev/null || mkdir -p /root/SimularFiles && cp /tmp/work/cities.png /root/SimularFiles/cities.png; ls -la /root/SimularFiles/cities.png\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Copying the contact sheet for viewing" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@130818732570.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_01Fn9B8QA4G3MaVmeL3WrnXw", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"echo HOME=$HOME; whoami; ls -la $HOME | head -40; find / -maxdepth 4 -name 'SimularFiles' -type d 2>/dev/null\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Locating the shared files directory" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@130832450127.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01K3PiPDsF7SZmiU6j9B44N3", + "name": "execute", + "input": { + "code": "var wins = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins, null, 2))", + "title": "Checking currently open windows" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@130849539278.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01W99Sk2PmwTp8DM4UdckLhu", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"ls /usr/bin | grep -iE 'eog|feh|viewer|xdg-open|display|ristretto|shotwell|gthumb|firefox|chrom' \" })\nconsole.log(r.stdout, r.stderr)", + "title": "Finding an available image viewer" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@130903181276.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_017Jmq3mgBRGCMpoSE7Cp1cX", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"DISPLAY=:0 nohup eog /tmp/work/cities.png > /dev/null 2>&1 & sleep 3; echo ok\" })\nawait wait({ waitTime: 2 })\nvar wins2 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins2, null, 2))", + "title": "Opening the city photo contact sheet" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@130922269512.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_01U8CHSoP8hrZ8gmANutQxzc", + "name": "execute", + "input": { + "code": "var eog = await machine.getAppWindow(103)\nawait eog.activate()\nawait wait({ waitTime: 1 })\nawait eog.screenshot()\nconsole.log(\"screenshot taken\")", + "title": "Capturing the contact sheet view" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@130944278434.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01JFgH4DqkBBdXoTaRAujnov", + "name": "execute", + "input": { + "code": "var py2 = `\nfrom PIL import Image\nim = Image.open(\"/tmp/work/city/city/picture3.jpg\").convert(\"RGB\")\nprint(im.size)\nim2 = im.copy(); im2.thumbnail((1400,900)); im2.save(\"/tmp/work/p3.png\")\n`;\nawait machine.writeToFile({ text: py2, path: \"/tmp/work/p3.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 p3.py && DISPLAY=:0 nohup eog /tmp/work/p3.png > /dev/null 2>&1 & sleep 4; echo ok\" })\nconsole.log(r.stdout, r.stderr)\nawait wait({ waitTime: 2 })\nvar wins3 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins3))", + "title": "Opening picture3 for a closer look" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@131016706705.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_01FPKy4GBtJajcedPazq6RTG", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"ls -la /tmp/work/city/city/ /tmp/work/filter/filter/\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Listing exact file names in both folders" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@131028640813.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01XKGkEMYMVKYHj38CJoLq76", + "name": "execute", + "input": { + "code": "var py3 = `\nfrom PIL import Image, ImageDraw\nimport glob, os\nim = Image.open(\"/tmp/work/city/city/picture3.jpeg\").convert(\"RGB\")\nim.thumbnail((1500,950)); im.save(\"/tmp/work/p3.png\")\nprint(\"p3 ok\", im.size)\n\ndef sheet(files, out, cols, tw=500, th=330, bg=(40,40,40)):\n rows = (len(files)+cols-1)//cols\n canvas = Image.new(\"RGB\", (cols*tw, rows*(th+24)), bg)\n d = ImageDraw.Draw(canvas)\n for i,f in enumerate(files):\n im = Image.open(f)\n im = im.convert(\"RGBA\")\n # composite over mid-gray checker to reveal transparency\n base = Image.new(\"RGBA\", im.size, (128,128,128,255))\n base.alpha_composite(im)\n im = base.convert(\"RGB\")\n im.thumbnail((tw-8, th-8))\n x = (i%cols)*tw + (tw-im.width)//2\n y = (i//cols)*(th+24) + 24 + (th-im.height)//2\n canvas.paste(im,(x,y))\n d.text(((i%cols)*tw+6, (i//cols)*(th+24)+6), os.path.basename(f), fill=(255,255,0))\n canvas.save(out); print(out, canvas.size)\n\nff = sorted(glob.glob(\"/tmp/work/filter/filter/filter*\"), key=lambda p: int(''.join(c for c in os.path.basename(p).split('.')[0] if c.isdigit())))\nprint(ff)\nsheet(ff[:5], \"/tmp/work/filters_a.png\", 3)\nsheet(ff[5:], \"/tmp/work/filters_b.png\", 3)\n`;\nawait machine.writeToFile({ text: py3, path: \"/tmp/work/p3.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 p3.py\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Preparing filter contact sheets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@131049799745.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_011WYh3DG8XCPhaF1eVG87y5", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"pkill -f eog; sleep 1; DISPLAY=:0 nohup eog /tmp/work/p3.png > /dev/null 2>&1 & sleep 4; echo ok\" })\nawait wait({ waitTime: 3 })\nvar wins4 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins4))", + "title": "Displaying picture3 full size" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@131107982974.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01KtfaDJvTiuF2JfgAHyuD9q", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"export DISPLAY=:0; setsid eog /tmp/work/p3.png < /dev/null > /tmp/eog.log 2>&1 & disown; sleep 5; cat /tmp/eog.log; DISPLAY=:0 wmctrl -l 2>/dev/null || xdotool search --name '' getwindowname %@ 2>/dev/null | head\" })\nconsole.log(r.stdout, r.stderr)\nawait wait({ waitTime: 2 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))", + "title": "Retrying to display picture3" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@131131528175.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_0172o6BbyxWHTdHsmMnRt2g1", + "name": "execute", + "input": { + "code": "var eog = await machine.getAppWindow(\"Image Viewer\")\nawait eog.activate()\nawait wait({ waitTime: 1 })\nawait eog.screenshot()\nconsole.log(\"ok\")", + "title": "Viewing picture3 in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@131147476748.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01CPuinBLFiudrW2Rf9dDkWb", + "name": "execute", + "input": { + "code": "var eog = await machine.getAppWindow(103)\nawait eog.activate()\nawait wait({ waitTime: 1.5 })\nawait eog.screenshot()\nconsole.log(\"ok\")", + "title": "Viewing picture3 in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@131209908936.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_012vsCajfaYjwqfF8goMAxGC", + "name": "execute", + "input": { + "code": "var pyA = `\nfrom PIL import Image\nimport numpy as np, glob, os, json\nff = sorted(glob.glob(\"/tmp/work/filter/filter/filter*\"), key=lambda p: int(''.join(c for c in os.path.basename(p).split('.')[0] if c.isdigit())))\nfor f in ff:\n im = Image.open(f)\n info = {\"file\": os.path.basename(f), \"size\": im.size, \"mode\": im.mode, \"format\": im.format}\n has_alpha = im.mode in (\"RGBA\",\"LA\",\"PA\") or (\"transparency\" in im.info)\n info[\"has_alpha_flag\"] = bool(has_alpha)\n rgba = im.convert(\"RGBA\")\n a = np.array(rgba)\n alpha = a[...,3]\n info[\"alpha_min\"] = int(alpha.min()); info[\"alpha_mean\"] = round(float(alpha.mean()),1)\n rgb = a[...,:3].astype(float)\n lum = rgb.mean(axis=2)\n info[\"lum_mean\"] = round(float(lum.mean()),1)\n info[\"lum_median\"] = round(float(np.median(lum)),1)\n info[\"pct_dark(<40)\"] = round(float((lum<40).mean()*100),1)\n info[\"pct_bright(>200)\"] = round(float((lum>200).mean()*100),1)\n # border sample\n b = np.concatenate([lum[0,:], lum[-1,:], lum[:,0], lum[:,-1]])\n info[\"border_lum_mean\"] = round(float(b.mean()),1)\n # saturation\n mx = rgb.max(axis=2); mn = rgb.min(axis=2)\n sat = np.where(mx>0, (mx-mn)/np.maximum(mx,1), 0)\n info[\"sat_mean\"] = round(float(sat.mean()),3)\n print(json.dumps(info))\n`;\nawait machine.writeToFile({ text: pyA, path: \"/tmp/work/an.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 an.py\" })\nconsole.log(r.stdout)\nconsole.log(r.stderr)", + "title": "Analyzing filter backgrounds and transparency" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@131247778108.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_01AYFK2EjK8nQ1H1xLg9AFd2", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"pkill -f 'eog ' ; sleep 1; echo done\" })\nvar r = await machine.exec({ command: \"export DISPLAY=:0; setsid eog /tmp/work/filters_a.png < /dev/null > /dev/null 2>&1 & disown; sleep 5; echo ok\" })\nawait wait({ waitTime: 2 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))", + "title": "Opening the first filter contact sheet" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@131318603728.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_0144JuGWDEsxpQJsm9Egmmcx", + "name": "execute", + "input": { + "code": "var eog = await machine.getAppWindow(103)\nawait eog.activate()\nawait wait({ waitTime: 1.5 })\nawait eog.screenshot()\nconsole.log(\"ok\")", + "title": "Viewing filters 1 through 5" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@131334448224.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01E9eWqHpCuDUWbU1rWP5Yp5", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"pkill -f 'eog /tmp' ; sleep 1; echo done\" })\nawait machine.exec({ command: \"export DISPLAY=:0; setsid eog /tmp/work/filters_b.png < /dev/null > /dev/null 2>&1 & disown; sleep 5; echo ok\" })\nawait wait({ waitTime: 2 })\nvar wl = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl))\nvar eog2 = await machine.getAppWindow(wl.find(w=>w.title===\"\")?.id ?? 103)\nawait eog2.activate(); await wait({ waitTime: 1.5 })\nawait eog2.screenshot()\nconsole.log(\"ok\")", + "title": "Viewing filters 6 through 10" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@131416411213.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_01UzsN3UU3XuEu2FyV3Gm1Ga", + "name": "execute", + "input": { + "code": "var pyB = `\nfrom PIL import Image\nimport numpy as np\n\n# Detect baked-in checkerboard: look for a regular alternating grid of two gray values\ndef checker_score(path):\n im = Image.open(path).convert(\"RGB\")\n a = np.array(im).astype(float)\n lum = a.mean(axis=2)\n h,w = lum.shape\n best = None\n for block in (4,5,6,7,8,10,12,16):\n # compare mean of \"even\" blocks vs \"odd\" blocks\n ys = (np.arange(h)//block)%2\n xs = (np.arange(w)//block)%2\n mask = (ys[:,None] ^ xs[None,:]).astype(bool)\n d = abs(lum[mask].mean() - lum[~mask].mean())\n if best is None or d > best[1]:\n best = (block, d)\n return best\n\nimport glob, os\nff = sorted(glob.glob(\"/tmp/work/filter/filter/filter*\"), key=lambda p: int(''.join(c for c in os.path.basename(p).split('.')[0] if c.isdigit())))\nfor f in ff:\n im = Image.open(f)\n real_alpha = im.mode in (\"RGBA\",\"LA\") or \"transparency\" in im.info\n b,d = checker_score(f)\n print(f\"{os.path.basename(f):16s} mode={im.mode:5s} size={str(im.size):14s} realAlpha={str(real_alpha):5s} checker(block={b}, delta={d:.2f})\")\n`;\nawait machine.writeToFile({ text: pyB, path: \"/tmp/work/chk.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 chk.py\" })\nconsole.log(r.stdout)\nconsole.log(r.stderr)", + "title": "Detecting baked-in checkerboard patterns" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@131453474139.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_01KG8b2gWW5AJjTnTeiBFf7q", + "name": "execute", + "input": { + "code": "var pyC = `\nfrom PIL import Image\nimport numpy as np, glob, os\nfrom collections import Counter\nff = sorted(glob.glob(\"/tmp/work/filter/filter/filter*\"), key=lambda p: int(''.join(c for c in os.path.basename(p).split('.')[0] if c.isdigit())))\nfor f in ff:\n im = Image.open(f).convert(\"RGB\")\n a = np.array(im)\n flat = a.reshape(-1,3)\n # sample for speed\n if len(flat) > 400000:\n idx = np.random.RandomState(0).choice(len(flat), 400000, replace=False)\n flat = flat[idx]\n c = Counter(map(tuple, flat))\n top = c.most_common(5)\n tot = len(flat)\n s = \", \".join(f\"{t[0]}:{100*t[1]/tot:.1f}%\" for t in top)\n print(f\"{os.path.basename(f):16s} uniq~{len(c):7d} top: {s}\")\n`;\nawait machine.writeToFile({ text: pyC, path: \"/tmp/work/hist.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 hist.py\" })\nconsole.log(r.stdout)\nconsole.log(r.stderr)", + "title": "Checking dominant colors in each filter" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@131518559649.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01KzYkaX2F2q5jyQVeMjdLaF", + "name": "execute", + "input": { + "code": "var pyD = `\nfrom PIL import Image, ImageDraw\nimport numpy as np\n\n# filter5 over dark bg, scaled 2x\nf5 = Image.open(\"/tmp/work/filter/filter/filter5.png\").convert(\"RGBA\")\nbg = Image.new(\"RGBA\", f5.size, (20,20,60,255))\nbg.alpha_composite(f5)\nf5v = bg.convert(\"RGB\").resize((f5.width*2, f5.height*2), Image.LANCZOS)\n\n# filter4: downscale full + 100% crop\nf4 = Image.open(\"/tmp/work/filter/filter/filter4.jpg\").convert(\"RGB\")\nf4s = f4.copy(); f4s.thumbnail((960,640))\nf4c = f4.crop((2000,1400,2960,2040))\nf4c = f4c.resize((960,640))\n\ncanvas = Image.new(\"RGB\", (960*2+30, 640*2+70), (25,25,25))\nd = ImageDraw.Draw(canvas)\ncanvas.paste(f5v.resize((960,640)), (0,25))\nd.text((5,5), \"filter5.png (rain? over dark bg, alpha composite)\", fill=(255,255,0))\ncanvas.paste(f4s, (990,25)); d.text((995,5), \"filter4.jpg full\", fill=(255,255,0))\ncanvas.paste(f4c, (0,690)); d.text((5,670), \"filter4.jpg 100% crop\", fill=(255,255,0))\n\nf5c = f5.crop((0,0,240,160))\nbg2 = Image.new(\"RGBA\", f5c.size, (20,20,60,255)); bg2.alpha_composite(f5c)\ncanvas.paste(bg2.convert(\"RGB\").resize((960,640), Image.NEAREST), (990,690))\nd.text((995,670), \"filter5 top-left quarter zoom\", fill=(255,255,0))\ncanvas.save(\"/tmp/work/verify1.png\"); print(canvas.size)\n`;\nawait machine.writeToFile({ text: pyD, path: \"/tmp/work/v1.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 v1.py\" })\nconsole.log(r.stdout, r.stderr)\nawait machine.exec({ command: \"pkill -f 'eog /tmp'; sleep 1; export DISPLAY=:0; setsid eog /tmp/work/verify1.png < /dev/null > /dev/null 2>&1 & disown; sleep 5; echo ok\" })\nawait wait({ waitTime: 2 })\nvar wl2 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl2))", + "title": "Preparing a detail view of filter4 and filter5" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@131557516120.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_01FfXA75Vj6hdTfzAmB2katX", + "name": "execute", + "input": { + "code": "await wait({ waitTime: 3 })\nvar wl3 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl3))\nvar cand = wl3.find(w=>w.title===\"\" && w.id!==49)\nconsole.log(\"cand:\", JSON.stringify(cand))\nvar eog3 = await machine.getAppWindow(cand.id)\nawait eog3.activate(); await wait({ waitTime: 1.5 })\nawait eog3.screenshot()\nconsole.log(\"ok\")", + "title": "Inspecting filter4 and filter5 details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@131616963539.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_013grTnsy4SzaKV3L1drHeZ1", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"pgrep -af eog; export DISPLAY=:0; setsid eog /tmp/work/verify1.png < /dev/null > /tmp/eog2.log 2>&1 & disown; sleep 6; pgrep -af eog; cat /tmp/eog2.log\" })\nconsole.log(r.stdout, r.stderr)\nawait wait({ waitTime: 3 })\nvar wl3 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl3))", + "title": "Relaunching the image viewer" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@131639290937.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01MJN9MJMEKWq1eSBsdi4g8X", + "name": "execute", + "input": { + "code": "var eog3 = await machine.getAppWindow(103)\nawait eog3.activate(); await wait({ waitTime: 1.5 })\nawait eog3.screenshot()\nconsole.log(\"ok\")", + "title": "Inspecting filter4 and filter5 details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@131656076980.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_0175B3PcHmdzDsPptyn1yiAu", + "name": "execute", + "input": { + "code": "var pyE = `\nfrom PIL import Image, ImageDraw\nfiles = [\"filter1.jpeg\",\"filter6.jpg\",\"filter10.jpg\",\"filter2.jpg\",\"filter3.jpg\",\"filter9.png\"]\ntw,th = 640,420\ncanvas = Image.new(\"RGB\",(tw*3+20, (th+24)*2+10),(25,25,25))\nd = ImageDraw.Draw(canvas)\nfor i,fn in enumerate(files):\n im = Image.open(\"/tmp/work/filter/filter/\"+fn).convert(\"RGB\")\n im.thumbnail((tw-6,th-6))\n x=(i%3)*(tw+5); y=(i//3)*(th+29)+24\n canvas.paste(im,(x+(tw-im.width)//2, y+(th-im.height)//2))\n d.text((x+4,(i//3)*(th+29)+4), fn, fill=(255,255,0))\ncanvas.save(\"/tmp/work/verify2.png\"); print(canvas.size)\n`;\nawait machine.writeToFile({ text: pyE, path: \"/tmp/work/v2.py\", overwrite: true })\nawait machine.exec({ command: \"cd /tmp/work && python3 v2.py && pkill -f 'eog /tmp'; sleep 1; export DISPLAY=:0; setsid eog /tmp/work/verify2.png < /dev/null > /dev/null 2>&1 & disown; sleep 6; echo ok\" })\nawait wait({ waitTime: 3 })\nvar eg = await machine.getAppWindow(103)\nawait eg.activate(); await wait({ waitTime: 1.5 })\nawait eg.screenshot()\nconsole.log(\"ok\")", + "title": "Reviewing the remaining filters" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@131727952336.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01JoGeUjEFnwAKyfjCP9CfM3", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"export DISPLAY=:0; setsid eog /tmp/work/verify2.png < /dev/null > /dev/null 2>&1 & disown; sleep 6; echo ok\" })\nawait wait({ waitTime: 3 })\nvar wl4 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl4))\nvar eg = await machine.getAppWindow(103)\nawait eg.activate(); await wait({ waitTime: 1.5 })\nawait eg.screenshot()\nconsole.log(\"ok\")", + "title": "Reviewing the remaining filters" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@131756333862.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01AbReQpZcgEn4vpeYGuqi4J", + "name": "execute", + "input": { + "code": "var findings = {\n hkPhoto: \"picture3.jpeg\", // Victoria Peak view: Bank of China Tower + IFC = Hong Kong\n N: 3,\n rain: { file: \"filter5.png\", M: 5, bg: \"true alpha transparency\", blend: \"Normal\" },\n snow: { file: \"filter4.jpg\", M: 4, bg: \"solid black\", blend: \"Screen\" },\n unusable: \"filter2,3,7,8,9 have transparency checkerboard baked into pixels (filter9=snow but unusable)\",\n others: \"filter1=fog cityscape, filter6=lens flare, filter10=sun glow\",\n out: [\"hk_rainy_pic3&filter5.png\", \"hk_snowy_pic3&filter4.png\"]\n}\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 -c \\\"\\nfrom PIL import Image\\nfor f in ['city/city/picture3.jpeg','filter/filter/filter4.jpg','filter/filter/filter5.png']:\\n im=Image.open(f); print(f, im.size, im.mode, round(im.size[0]/im.size[1],4))\\n\\\" ; echo '---'; gimp --version; echo '---'; ls ~/.config/GIMP 2>/dev/null; ls ~/.gimp* 2>/dev/null\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Recording findings and checking GIMP version" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@131827997722.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_016BEQUjfhYurXGs6RWR5uFK", + "name": "execute", + "input": { + "code": "var scm = `\n; Build 1920x1080 weather composites for weather_of_hongkong.pptx\n(define (hk-composite base-path filter-path blend-mode out-path)\n (let* ((img (car (gimp-file-load RUN-NONINTERACTIVE base-path base-path))))\n (gimp-context-push)\n (gimp-context-set-interpolation INTERPOLATION-CUBIC)\n ; normalise base to exactly 1920x1080\n (gimp-image-scale img 1920 1080)\n (let* ((flt (car (gimp-file-load-layer RUN-NONINTERACTIVE img filter-path))))\n (gimp-image-insert-layer img flt 0 -1)\n (gimp-layer-scale flt 1920 1080 FALSE)\n (gimp-layer-set-offsets flt 0 0)\n (gimp-layer-set-mode flt blend-mode)\n (gimp-layer-set-opacity flt 100.0)\n (gimp-image-flatten img)\n (let* ((final (car (gimp-image-get-active-layer img))))\n (file-png-save RUN-NONINTERACTIVE img final out-path out-path\n 0 9 1 1 1 1 1)))\n (gimp-context-pop)\n (gimp-image-delete img)\n (gimp-message (string-append \"saved \" out-path))))\n\n(hk-composite \"/tmp/work/city/city/picture3.jpeg\"\n \"/tmp/work/filter/filter/filter5.png\"\n LAYER-MODE-NORMAL\n \"/home/user/Desktop/hk_rainy_pic3&filter5.png\")\n\n(hk-composite \"/tmp/work/city/city/picture3.jpeg\"\n \"/tmp/work/filter/filter/filter4.jpg\"\n LAYER-MODE-SCREEN\n \"/home/user/Desktop/hk_snowy_pic3&filter4.png\")\n`;\nvar p = await machine.writeToFile({ text: scm, path: \"/tmp/work/composite.scm\", overwrite: true })\nconsole.log(\"written to\", p)\nvar r = await machine.exec({ command: \"cat /tmp/work/composite.scm | head -5\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Writing the GIMP compositing script" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@131915472256.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01QmZkHfdtAKNDQfij3h4Zmo", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"pkill -f 'eog /tmp'; echo ok\" })\nawait wait({ waitTime: 1 })\nvar gimp = await machine.getAppWindow(\"GNU Image Manipulation Program\")\nawait gimp.activate()\nawait wait({ waitTime: 2 })\nvar gs = await gimp.snapshot()\nconsole.log(gs.snapshot.outline())", + "title": "Opening GIMP and reading its menus" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@131939523102.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_01E4jhK6LK1DsNB2tuh16oMs", + "name": "execute", + "input": { + "code": "console.log(gs.snapshot.grep(\"Console\", { context: 1 }))", + "title": "Locating the Script-Fu Console menu entry" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@131953435375.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_01FMSbxhZPGc8zJkvuRcgWA5", + "name": "execute", + "input": { + "code": "console.log(gs.snapshot.read(860, 880))", + "title": "Reading the Script-Fu submenu structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@132002357145.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_01G1sBavWXtBjwpmv5Zh3qrt", + "name": "execute", + "input": { + "code": "var hits = await gs.snapshot.find(\"Filters top-level menu bar item\")\nconsole.log(JSON.stringify(hits, null, 2))\nconsole.log(gs.snapshot.grep(\"menubar|menu \\\"Filters\\\"\", { context: 3 }))", + "title": "Finding the Filters menu in the menu bar" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@132018331387.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_018zE4FUzoF9f5LdJHBWaLpX", + "name": "execute", + "input": { + "code": "await gimp.click({ ref: \"555\" })\nawait wait({ waitTime: 1.5 })\nawait gimp.screenshot()\nconsole.log(\"opened Filters menu\")", + "title": "Opening the Filters menu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@132030047377.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_01HEah4AUidVijFgTV8BKaUu", + "name": "execute", + "input": { + "code": "await machine.click({ x: 506, y: 554 })\nawait wait({ waitTime: 1.5 })\nawait gimp.screenshot()\nconsole.log(\"Script-Fu submenu\")", + "title": "Opening the Script-Fu submenu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@132043730168.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01M3oCPtgqdAo7a1THhDJ9wy", + "name": "execute", + "input": { + "code": "await machine.move({ x: 600, y: 554 })\nawait wait({ waitTime: 1 })\nawait machine.move({ x: 725, y: 555 })\nawait wait({ waitTime: 1.5 })\nawait gimp.screenshot()\nconsole.log(\"hovered\")", + "title": "Expanding the Script-Fu submenu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@132059807971.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_01QbEbuZa3eauzzdssw83Sao", + "name": "execute", + "input": { + "code": "await gimp.press({ key: \"rightArrow\" })\nawait wait({ waitTime: 1.5 })\nawait gimp.screenshot()\nconsole.log(\"expanded\")", + "title": "Expanding the Script-Fu submenu with keyboard" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@132113638766.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_01QXoHvsVQtAtpUyobvqpGvY", + "name": "execute", + "input": { + "code": "await machine.click({ x: 560, y: 555 })\nawait wait({ waitTime: 2 })\nawait gimp.screenshot()\nconsole.log(\"clicked Script-Fu\")", + "title": "Clicking the Script-Fu menu item" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@132129183708.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_013Ekxhe3qeB8s9jKbMwodWu", + "name": "execute", + "input": { + "code": "await machine.click({ x: 796, y: 554 })\nawait wait({ waitTime: 3 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))\nawait gimp.screenshot()", + "title": "Opening the Script-Fu Console" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@132146950754.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_01KpccNXca31ocxRzGvcNPKh", + "name": "execute", + "input": { + "code": "var sfc = await machine.getAppWindow(\"Script-Fu Console\")\nawait sfc.activate()\nawait wait({ waitTime: 2 })\nvar ss = await sfc.snapshot()\nconsole.log(ss.snapshot.outline())", + "title": "Bringing the Script-Fu Console to front" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@132209064113.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_01NhUUrW6JRK9hNxJWFkzCJN", + "name": "execute", + "input": { + "code": "await machine.click({ x: 950, y: 748 })\nawait wait({ waitTime: 1 })\nawait machine.type({ text: '(load \"/tmp/work/composite.scm\")' })\nawait wait({ waitTime: 1 })\nawait machine.press({ key: \"return\" })\nawait wait({ waitTime: 10 })\nawait sfc.screenshot()\nconsole.log(\"ran\")", + "title": "Running the compositing script in GIMP" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@132235922651.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_01B9z4zJuCj6red3DdSthExD", + "name": "execute", + "input": { + "code": "await wait({ waitTime: 8 })\nvar r = await machine.exec({ command: \"ls -la ~/Desktop/ | grep -i 'hk_' ; echo '---'; cd ~/Desktop && for f in hk_*.png; do echo -n \\\"$f : \\\"; python3 -c \\\"import sys; from PIL import Image; im=Image.open(sys.argv[1]); print(im.size, im.mode, im.format)\\\" \\\"$f\\\"; done\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Verifying the generated composite files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@132258214377.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_01KfZM6MERQQXQs8vG1hJcS4", + "name": "execute", + "input": { + "code": "var pyF = `\nfrom PIL import Image, ImageDraw\na = Image.open(\"/home/user/Desktop/hk_rainy_pic3&filter5.png\").convert(\"RGB\")\nb = Image.open(\"/home/user/Desktop/hk_snowy_pic3&filter4.png\").convert(\"RGB\")\na2 = a.copy(); a2.thumbnail((940,540))\nb2 = b.copy(); b2.thumbnail((940,540))\nac = a.crop((600,300,1160,615)).resize((940,529))\nbc = b.crop((600,300,1160,615)).resize((940,529))\nc = Image.new(\"RGB\",(1900,1180),(20,20,20)); d=ImageDraw.Draw(c)\nc.paste(a2,(5,25)); d.text((8,5),\"hk_rainy_pic3&filter5.png (Normal)\",fill=(255,255,0))\nc.paste(b2,(955,25)); d.text((958,5),\"hk_snowy_pic3&filter4.png (Screen)\",fill=(255,255,0))\nc.paste(ac,(5,600)); d.text((8,580),\"rainy 100% crop\",fill=(255,255,0))\nc.paste(bc,(955,600)); d.text((958,580),\"snowy 100% crop\",fill=(255,255,0))\nc.save(\"/tmp/work/verify3.png\"); print(\"ok\")\n`;\nawait machine.writeToFile({ text: pyF, path: \"/tmp/work/v3.py\", overwrite: true })\nawait machine.exec({ command: \"cd /tmp/work && python3 v3.py && export DISPLAY=:0 && setsid eog /tmp/work/verify3.png < /dev/null > /dev/null 2>&1 & disown; sleep 6; echo ok\" })\nawait wait({ waitTime: 3 })\nvar wl5 = await machine.listAppWindows()\nconsole.log(JSON.stringify(wl5))", + "title": "Preparing a visual check of both composites" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@132326332453.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_01T7WSohAFKT2jVVRkiNRENq", + "name": "execute", + "input": { + "code": "var ev = await machine.getAppWindow(135)\nawait ev.activate(); await wait({ waitTime: 2 })\nawait ev.screenshot()\nconsole.log(\"ok\")", + "title": "Inspecting the finished composites" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@132346645121.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_01KRSmDV9ETxNy9njCGsgpEn", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"pkill -f 'eog /tmp'; echo ok\" })\nvar pyG = `\nfrom pptx import Presentation\nfrom pptx.util import Emu\nimport sys\np = \"/home/user/Desktop/weather_of_hongkong.pptx\"\nprs = Presentation(p)\nprint(\"slide size:\", prs.slide_width, prs.slide_height, \"=\", prs.slide_width/914400, \"x\", prs.slide_height/914400, \"in\")\nprint(\"EMU px@96:\", prs.slide_width/9525, prs.slide_height/9525)\nfor i, s in enumerate(prs.slides, 1):\n print(\"=\"*60)\n print(\"SLIDE\", i, \" layout:\", s.slide_layout.name)\n for sh in s.shapes:\n txt = \"\"\n if sh.has_text_frame:\n txt = \" | \".join(r.text for pa in sh.text_frame.paragraphs for r in pa.runs)\n print(f\" [{sh.shape_type}] name={sh.name!r} txt={txt[:120]!r}\")\n # background xml\n bg = s._element.find('{http://schemas.openxmlformats.org/presentationml/2006/main}cSld')\n import re\n x = s._element.xml\n m = re.search(r'.*?', x, re.S)\n print(\" BG:\", (m.group(0)[:400] if m else None))\n`;\nawait machine.writeToFile({ text: pyG, path: \"/tmp/work/inspect.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 inspect.py 2>&1\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Inspecting the PowerPoint slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@132429851714.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": { + "id": "toolu_bdrk_0132TyWnguYarijazzfUrQ9p", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"rm -rf /tmp/pp && mkdir -p /tmp/pp && cd /tmp/pp && unzip -o ~/Desktop/weather_of_hongkong.pptx > /dev/null && find . -type f | sort && echo '=== presentation.xml ===' && python3 -c \\\"import re,sys;d=open('ppt/presentation.xml').read();print(d[:1500])\\\"\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Unpacking the presentation to read its XML" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_48_20260923@132441943776.png", + "osworld_action": "WAIT" + }, + { + "step_num": 49, + "action": { + "id": "toolu_bdrk_01QZbeKaeDYbxHJs7jGn5Vhx", + "name": "execute", + "input": { + "code": "var pyH = `\nimport re, glob\nfor i in range(1,7):\n p = f\"/tmp/pp/ppt/slides/slide{i}.xml\"\n d = open(p, encoding=\"utf-8\").read()\n texts = re.findall(r'(.*?)', d, re.S)\n bg = re.search(r'.*?', d, re.S)\n print(\"=\"*70)\n print(f\"slide{i}.xml len={len(d)}\")\n print(\" TEXT:\", \" / \".join(t.strip() for t in texts if t.strip())[:300])\n print(\" BG :\", bg.group(0) if bg else None)\n print(\" RELS:\", open(f\"/tmp/pp/ppt/slides/_rels/slide{i}.xml.rels\", encoding=\"utf-8\").read())\n`;\nawait machine.writeToFile({ text: pyH, path: \"/tmp/work/insp2.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 insp2.py 2>&1\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Reading slide text and existing backgrounds" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_49_20260923@132456345646.png", + "osworld_action": "WAIT" + }, + { + "step_num": 50, + "action": { + "id": "toolu_bdrk_01LzoAq354Q6G8AxuJhdfF8q", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp/pp && python3 -c \\\"\\nd=open('ppt/slides/slide5.xml',encoding='utf-8').read()\\nprint('--- slide5 head ---'); print(d[:700])\\nd6=open('ppt/slides/slide6.xml',encoding='utf-8').read()\\nprint('--- slide6 head ---'); print(d6[:700])\\nprint('--- content types ---')\\nprint(open('[Content_Types].xml',encoding='utf-8').read())\\n\\\" ; ls -la /tmp/pp/ppt/media/\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Reading slide XML headers and content types" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_50_20260923@132539468310.png", + "osworld_action": "WAIT" + }, + { + "step_num": 51, + "action": { + "id": "toolu_bdrk_01L3j5Hmcmz1YEuxncdMcoWZ", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp/pp && python3 -c \\\"\\nimport re\\nfor i in (3,4,5,6):\\n d=open(f'ppt/slides/slide{i}.xml',encoding='utf-8').read()\\n m=re.search(r'.*?',d,re.S)\\n print(f'slide{i} BG:', m.group(0) if m else 'NONE')\\n print(f'slide{i} RELS:', open(f'ppt/slides/_rels/slide{i}.xml.rels',encoding='utf-8').read().replace('><','>\\\\n <'))\\n print()\\n\\\"\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Comparing background XML on slides 3 to 6" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_51_20260923@132556619593.png", + "osworld_action": "WAIT" + }, + { + "step_num": 52, + "action": { + "id": "toolu_bdrk_01F7H79AeUzeMwXx46VnzxJK", + "name": "execute", + "input": { + "code": "var imp = await machine.getAppWindow(\"weather_of_hongkong.pptx - LibreOffice Impress\")\nawait imp.activate()\nawait wait({ waitTime: 2 })\nawait imp.screenshot()\nconsole.log(\"impress active\")", + "title": "Bringing LibreOffice Impress to front" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_52_20260923@132622878849.png", + "osworld_action": "WAIT" + }, + { + "step_num": 53, + "action": { + "id": "toolu_bdrk_01DEtKeej6mWxQrcKBBuv4T4", + "name": "execute", + "input": { + "code": "await imp.press({ key: \"ctrl+q\" })\nawait wait({ waitTime: 4 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))\nvar r = await machine.exec({ command: \"pgrep -af soffice | head; ls -la ~/Desktop | grep -i lock\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Closing LibreOffice Impress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_53_20260923@132647335900.png", + "osworld_action": "WAIT" + }, + { + "step_num": 54, + "action": { + "id": "toolu_bdrk_01587QRDXyLREF2Dus9GujWn", + "name": "execute", + "input": { + "code": "var pyPatch = `\nimport zipfile, shutil, re, os, sys\n\nSRC = \"/home/user/Desktop/weather_of_hongkong.pptx\"\nBAK = \"/tmp/work/weather_of_hongkong.BACKUP.pptx\"\nTMP = \"/tmp/work/out.pptx\"\nDESK = \"/home/user/Desktop/\"\n\nif not os.path.exists(BAK):\n shutil.copy2(SRC, BAK)\n print(\"backup ->\", BAK)\n\nzin = zipfile.ZipFile(BAK, \"r\")\n\n# --- template: copy slide3's picture background verbatim, only change alpha ---\ns3 = zin.read(\"ppt/slides/slide3.xml\").decode(\"utf-8\")\ntpl = re.search(r\".*?\", s3, re.S).group(0)\nprint(\"template BG:\", tpl)\nnewbg = tpl.replace('amt=\"50000\"', 'amt=\"60000\"') # 40% transparency => 60% alpha\nassert 'amt=\"60000\"' in newbg, \"alpha replace failed\"\nassert 'r:embed=\"rId3\"' in newbg, \"unexpected rId in template\"\nprint(\"new BG :\", newbg)\n\ns3r = zin.read(\"ppt/slides/_rels/slide3.xml.rels\").decode(\"utf-8\")\nrelm = re.search(r']*/>', s3r).group(0)\nprint(\"template REL:\", relm)\n\njobs = {\n 5: (\"hk_rainy_pic3&filter5.png\", \"image5.png\"),\n 6: (\"hk_snowy_pic3&filter4.png\", \"image6.png\"),\n}\n\npatched = {}\nfor n,(desk_name, media_name) in jobs.items():\n # ---- slide xml ----\n d = zin.read(f\"ppt/slides/slide{n}.xml\").decode(\"utf-8\")\n if \"\" in d:\n d2 = re.sub(r\".*?\", newbg, d, count=1, flags=re.S)\n else:\n assert \"\" in d, f\"slide{n}: unexpected cSld layout\"\n d2 = d.replace(\"\", \"\" + newbg + \"\", 1)\n assert d2 != d and 'amt=\"60000\"' in d2, f\"slide{n} bg patch failed\"\n patched[f\"ppt/slides/slide{n}.xml\"] = d2.encode(\"utf-8\")\n\n # ---- rels ----\n rl = zin.read(f\"ppt/slides/_rels/slide{n}.xml.rels\").decode(\"utf-8\")\n assert 'Id=\"rId3\"' not in rl, f\"slide{n}: rId3 already used\"\n newrel = relm.replace(\"image3.png\", media_name).replace(\"image4.png\", media_name)\n assert media_name in newrel, \"rel target rewrite failed: \" + newrel\n rl2 = rl.replace(\"\", newrel + \"\")\n patched[f\"ppt/slides/_rels/slide{n}.xml.rels\"] = rl2.encode(\"utf-8\")\n print(f\"slide{n} rel ->\", newrel)\n\n # ---- media ----\n patched[f\"ppt/media/{media_name}\"] = open(DESK + desk_name, \"rb\").read()\n\n# --- rebuild zip preserving order + untouched bytes ---\nnames = zin.namelist()\nadded = set()\nzout = zipfile.ZipFile(TMP, \"w\", zipfile.ZIP_DEFLATED)\nfor item in zin.infolist():\n if item.filename in patched:\n zout.writestr(item, patched[item.filename]); added.add(item.filename)\n else:\n zout.writestr(item, zin.read(item.filename))\nfor k,v in patched.items():\n if k not in added:\n zout.writestr(k, v); print(\"added new entry:\", k, len(v))\nzout.close(); zin.close()\n\n# sanity: content types already declares png\nct = zipfile.ZipFile(TMP).read(\"[Content_Types].xml\").decode(\"utf-8\")\nassert 'Extension=\"png\"' in ct, \"png default missing\"\nshutil.move(TMP, SRC)\nprint(\"WROTE\", SRC, os.path.getsize(SRC))\n`;\nawait machine.writeToFile({ text: pyPatch, path: \"/tmp/work/patch.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 patch.py 2>&1\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Applying the new backgrounds at 40% transparency" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_54_20260923@132720664646.png", + "osworld_action": "WAIT" + }, + { + "step_num": 55, + "action": { + "id": "toolu_bdrk_0194ZowdtLXmS63qkxMQiThD", + "name": "execute", + "input": { + "code": "var pyV = `\nimport zipfile, re, hashlib, os\nSRC=\"/home/user/Desktop/weather_of_hongkong.pptx\"\nBAK=\"/tmp/work/weather_of_hongkong.BACKUP.pptx\"\na=zipfile.ZipFile(BAK); b=zipfile.ZipFile(SRC)\nprint(\"zip test:\", b.testzip())\nna=set(a.namelist()); nb=set(b.namelist())\nprint(\"added entries:\", sorted(nb-na))\nprint(\"removed entries:\", sorted(na-nb))\ndiff=[n for n in sorted(na&nb) if a.read(n)!=b.read(n)]\nprint(\"CHANGED entries:\", diff)\nprint()\nfor i in (1,2,3,4,5,6):\n d=b.read(f\"ppt/slides/slide{i}.xml\").decode(\"utf-8\")\n m=re.search(r\".*?\",d,re.S)\n t=\" / \".join(x.strip() for x in re.findall(r\"(.*?)\",d,re.S) if x.strip())\n print(f\"slide{i}: {t[:60]!r}\")\n print(\" bg:\", m.group(0) if m else None)\n r=b.read(f\"ppt/slides/_rels/slide{i}.xml.rels\").decode(\"utf-8\")\n for rel in re.findall(r']*Target=\"([^\"]+)\"',r):\n if \"media\" in rel[1]: print(\" img rel:\", rel)\nprint()\n# confirm embedded media matches the Desktop composites byte-for-byte\nfor media, desk in ((\"ppt/media/image5.png\",\"hk_rainy_pic3&filter5.png\"),\n (\"ppt/media/image6.png\",\"hk_snowy_pic3&filter4.png\")):\n h1=hashlib.md5(b.read(media)).hexdigest()\n h2=hashlib.md5(open(\"/home/user/Desktop/\"+desk,\"rb\").read()).hexdigest()\n print(f\"{media} == {desk} ? {h1==h2} ({h1})\")\n`;\nawait machine.writeToFile({ text: pyV, path: \"/tmp/work/verify.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 verify.py 2>&1\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Verifying only the two target slides changed" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_55_20260923@132741710289.png", + "osworld_action": "WAIT" + }, + { + "step_num": 56, + "action": { + "id": "toolu_bdrk_01WicL7L7bx43yhAkvT4kQ1Z", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"export DISPLAY=:0; setsid soffice --impress /home/user/Desktop/weather_of_hongkong.pptx < /dev/null > /dev/null 2>&1 & disown; sleep 18; echo ok\" })\nawait wait({ waitTime: 6 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))", + "title": "Reopening the presentation to check rendering" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_56_20260923@132827296384.png", + "osworld_action": "WAIT" + }, + { + "step_num": 57, + "action": { + "id": "toolu_bdrk_01HVxRRYBp7XZkgKz1nkJrTj", + "name": "execute", + "input": { + "code": "var imp2 = await machine.getAppWindow(\"weather_of_hongkong.pptx - LibreOffice Impress\")\nawait imp2.activate()\nawait wait({ waitTime: 3 })\nawait imp2.screenshot()\nconsole.log(\"ok\")", + "title": "Checking how the new backgrounds render" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_57_20260923@132845881480.png", + "osworld_action": "WAIT" + }, + { + "step_num": 58, + "action": { + "id": "toolu_bdrk_0131nUoGMQHCcrH8uuVpfE5L", + "name": "execute", + "input": { + "code": "await machine.click({ x: 176, y: 652 })\nawait wait({ waitTime: 3 })\nawait imp2.screenshot()\nconsole.log(\"slide5\")", + "title": "Opening the Rainy Hong Kong slide" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_58_20260923@132909357370.png", + "osworld_action": "WAIT" + }, + { + "step_num": 59, + "action": { + "id": "toolu_bdrk_01X7J2a1SNGYeyEiD8jznQfw", + "name": "execute", + "input": { + "code": "await machine.click({ x: 176, y: 750 })\nawait wait({ waitTime: 3 })\nawait imp2.screenshot()\nconsole.log(\"slide6\")", + "title": "Opening the Snowy Hong Kong slide" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_59_20260923@132931110237.png", + "osworld_action": "WAIT" + }, + { + "step_num": 60, + "action": { + "id": "toolu_bdrk_01LangxoMqr9dfJ3syppBaNm", + "name": "execute", + "input": { + "code": "var s2 = await imp2.snapshot()\nvar m = await s2.snapshot.find(\"Slide top-level menu in the menu bar\")\nconsole.log(JSON.stringify(m))\nconsole.log(s2.snapshot.grep('menu \"Slide\"', { context: 2 }))", + "title": "Finding the Slide menu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_60_20260923@133007755113.png", + "osworld_action": "WAIT" + }, + { + "step_num": 61, + "action": { + "id": "toolu_bdrk_01LRHAivFtG8n2TJ6mvkCDqz", + "name": "execute", + "input": { + "code": "await imp2.press({ key: \"escape\" })\nawait wait({ waitTime: 1 })\nawait imp2.press({ key: \"escape\" })\nawait wait({ waitTime: 1 })\nawait machine.click({ x: 346, y: 76 }) // Slide menu\nawait wait({ waitTime: 2 })\nawait imp2.screenshot()\nconsole.log(\"slide menu\")", + "title": "Opening the Slide menu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_61_20260923@133039758545.png", + "osworld_action": "WAIT" + }, + { + "step_num": 62, + "action": { + "id": "toolu_bdrk_01PUwX2ZBcn1Mi5rX5sLmMFy", + "name": "execute", + "input": { + "code": "await machine.click({ x: 412, y: 278 }) // Slide Properties...\nawait wait({ waitTime: 3 })\nawait imp2.screenshot()\nconsole.log(\"slide properties\")", + "title": "Opening Slide Properties dialog" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_62_20260923@133102000808.png", + "osworld_action": "WAIT" + }, + { + "step_num": 63, + "action": { + "id": "toolu_bdrk_01Fps2Jxitur23rBHA6EbizC", + "name": "execute", + "input": { + "code": "await machine.click({ x: 817, y: 295 }) // Transparency tab\nawait wait({ waitTime: 2 })\nawait imp2.screenshot()\nconsole.log(\"transparency tab\")", + "title": "Checking the Transparency tab value" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_63_20260923@133120525784.png", + "osworld_action": "WAIT" + }, + { + "step_num": 64, + "action": { + "id": "toolu_bdrk_0144QXLfhfNBdSYXkPCvoCZX", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1282, y: 850 }) // Cancel\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 176, y: 652 }) // slide 5 in panel\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 346, y: 76 }) // Slide menu\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 412, y: 278 }) // Slide Properties...\nawait wait({ waitTime: 3 })\nawait machine.click({ x: 817, y: 295 }) // Transparency tab\nawait wait({ waitTime: 2 })\nawait imp2.screenshot()\nconsole.log(\"slide5 transparency\")", + "title": "Verifying slide 5 transparency is 40%" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_64_20260923@133149986850.png", + "osworld_action": "WAIT" + }, + { + "step_num": 65, + "action": { + "id": "toolu_bdrk_011n3bPmLsN89CYgvkvDW6st", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1282, y: 850 }) // Cancel\nawait wait({ waitTime: 2 })\nawait machine.press({ key: \"escape\" })\nawait wait({ waitTime: 1 })\nawait imp2.press({ key: \"ctrl+q\" })\nawait wait({ waitTime: 5 })\nconsole.log(JSON.stringify(await machine.listAppWindows()))\nawait imp2.screenshot().catch(e=>console.log(\"window gone (expected)\"))", + "title": "Closing the presentation without saving" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_65_20260923@133220022647.png", + "osworld_action": "WAIT" + }, + { + "step_num": 66, + "action": { + "id": "toolu_bdrk_01T1BFy686J7RHX1f9uhHUn2", + "name": "execute", + "input": { + "code": "var pyFinal = `\nimport zipfile, re, hashlib, os\nfrom PIL import Image\nimport numpy as np\n\nSRC=\"/home/user/Desktop/weather_of_hongkong.pptx\"\nprint(\"=== DESKTOP DELIVERABLES ===\")\nfor f in (\"hk_rainy_pic3&filter5.png\",\"hk_snowy_pic3&filter4.png\"):\n p=\"/home/user/Desktop/\"+f\n im=Image.open(p)\n print(f\" {f} exists={os.path.exists(p)} size={im.size} mode={im.mode} bytes={os.path.getsize(p)}\")\n assert im.size==(1920,1080), \"WRONG SIZE\"\n\nprint()\nprint(\"=== PPTX:\", SRC, os.path.getsize(SRC), \"bytes ===\")\nz=zipfile.ZipFile(SRC)\nprint(\" zip integrity:\", \"OK\" if z.testzip() is None else \"CORRUPT\")\nfor i in range(1,7):\n d=z.read(f\"ppt/slides/slide{i}.xml\").decode(\"utf-8\")\n t=\" / \".join(x.strip() for x in re.findall(r\"(.*?)\",d,re.S) if x.strip())\n bg=re.search(r\".*?\",d,re.S)\n amt=re.search(r'alphaModFix amt=\"(\\\\d+)\"',bg.group(0)) if bg else None\n rid=re.search(r'r:embed=\"(rId\\\\d+)\"',bg.group(0)) if bg else None\n tgt=None\n if rid:\n rels=z.read(f\"ppt/slides/_rels/slide{i}.xml.rels\").decode(\"utf-8\")\n mm=re.search(r'Id=\"%s\"[^>]*Target=\"([^\"]+)\"'%rid.group(1),rels)\n tgt=mm.group(1) if mm else None\n trans = f\"{(100000-int(amt.group(1)))/1000:g}%\" if amt else \"-\"\n print(f\" slide{i}: {t[:34]:36s} bg={'picture' if (bg and 'blipFill' in bg.group(0)) else ('solid' if bg else 'none')\n :7s} transparency={trans:6s} img={tgt}\")\n\nprint()\nprint(\"=== BACKGROUND IMAGES MATCH DESKTOP FILES ===\")\nfor media,desk in ((\"ppt/media/image5.png\",\"hk_rainy_pic3&filter5.png\"),\n (\"ppt/media/image6.png\",\"hk_snowy_pic3&filter4.png\")):\n h1=hashlib.md5(z.read(media)).hexdigest()\n h2=hashlib.md5(open(\"/home/user/Desktop/\"+desk,\"rb\").read()).hexdigest()\n print(f\" {media} == {desk}: {h1==h2}\")\n\nprint()\nprint(\"=== NOTHING ELSE CHANGED (vs pre-edit backup) ===\")\na=zipfile.ZipFile(\"/tmp/work/weather_of_hongkong.BACKUP.pptx\")\nna,nb=set(a.namelist()),set(z.namelist())\nprint(\" new entries :\", sorted(nb-na))\nprint(\" lost entries:\", sorted(na-nb))\nprint(\" modified :\", [n for n in sorted(na&nb) if a.read(n)!=z.read(n)])\n`;\nawait machine.writeToFile({ text: pyFinal, path: \"/tmp/work/final.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 final.py 2>&1; echo; echo '--- Desktop listing ---'; ls -la ~/Desktop\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Final verification of all deliverables" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_66_20260923@133245341928.png", + "osworld_action": "WAIT" + }, + { + "step_num": 67, + "action": { + "id": "toolu_bdrk_0155otSVmnc6uB7nYVXGNUsF", + "name": "execute", + "input": { + "code": "var pyFinal2 = `\nimport zipfile, re, hashlib, os\nfrom PIL import Image\n\nSRC=\"/home/user/Desktop/weather_of_hongkong.pptx\"\nprint(\"=== DESKTOP DELIVERABLES ===\")\nfor f in (\"hk_rainy_pic3&filter5.png\",\"hk_snowy_pic3&filter4.png\"):\n p=\"/home/user/Desktop/\"+f\n im=Image.open(p)\n ok = im.size==(1920,1080)\n print(\" %-30s exists=%s size=%s mode=%s bytes=%d 1920x1080=%s\" % (f, os.path.exists(p), im.size, im.mode, os.path.getsize(p), ok))\n assert ok\n\nprint()\nprint(\"=== PPTX: %s (%d bytes) ===\" % (SRC, os.path.getsize(SRC)))\nz=zipfile.ZipFile(SRC)\nprint(\" zip integrity:\", \"OK\" if z.testzip() is None else \"CORRUPT\")\nfor i in range(1,7):\n d=z.read(\"ppt/slides/slide%d.xml\"%i).decode(\"utf-8\")\n t=\" / \".join(x.strip() for x in re.findall(r\"(.*?)\",d,re.S) if x.strip())\n bgm=re.search(r\".*?\",d,re.S)\n bg = bgm.group(0) if bgm else \"\"\n kind = \"picture\" if \"blipFill\" in bg else (\"solid\" if bg else \"none\")\n amt=re.search(r'alphaModFix amt=\"(\\\\d+)\"', bg)\n trans = \"%g%%\" % ((100000-int(amt.group(1)))/1000.0) if amt else \"-\"\n rid=re.search(r'r:embed=\"(rId\\\\d+)\"', bg)\n tgt=\"-\"\n if rid:\n rels=z.read(\"ppt/slides/_rels/slide%d.xml.rels\"%i).decode(\"utf-8\")\n mm=re.search(r'Id=\"'+rid.group(1)+r'\"[^>]*Target=\"([^\"]+)\"', rels)\n tgt=mm.group(1) if mm else \"?\"\n print(\" slide%d: %-36s bg=%-8s transparency=%-6s img=%s\" % (i, t[:36], kind, trans, tgt))\n\nprint()\nprint(\"=== EMBEDDED BACKGROUNDS == DESKTOP FILES ===\")\nfor media,desk in ((\"ppt/media/image5.png\",\"hk_rainy_pic3&filter5.png\"),\n (\"ppt/media/image6.png\",\"hk_snowy_pic3&filter4.png\")):\n h1=hashlib.md5(z.read(media)).hexdigest()\n h2=hashlib.md5(open(\"/home/user/Desktop/\"+desk,\"rb\").read()).hexdigest()\n print(\" %s == %s : %s\" % (media, desk, h1==h2))\n\nprint()\nprint(\"=== NOTHING ELSE CHANGED (vs pre-edit backup) ===\")\na=zipfile.ZipFile(\"/tmp/work/weather_of_hongkong.BACKUP.pptx\")\nna,nb=set(a.namelist()),set(z.namelist())\nprint(\" new entries :\", sorted(nb-na))\nprint(\" lost entries:\", sorted(na-nb))\nprint(\" modified :\", [n for n in sorted(na&nb) if a.read(n)!=z.read(n)])\n`;\nawait machine.writeToFile({ text: pyFinal2, path: \"/tmp/work/final2.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp/work && python3 final2.py 2>&1\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Final verification of all deliverables" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_67_20260923@133308316974.png", + "osworld_action": "WAIT" + }, + { + "step_num": 68, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_68_20260923@133329480615.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/004.json b/demo/data/official/sai_traj/004.json new file mode 100644 index 0000000..27e14d7 --- /dev/null +++ b/demo/data/official/sai_traj/004.json @@ -0,0 +1,723 @@ +{ + "task": "004", + "run": "v21-r12-20260923T074850Z", + "score": 0.8, + "mean": 0.8, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-r12-20260923T074850Z/004/traj.jsonl", + "fetched_at": "2026-10-26T06:55:14Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01S8n5sNkjCE3dGuD3caeJVe", + "name": "execute", + "input": { + "title": "Checking open windows on the desktop", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar windows = await machine.listAppWindows()\nconsole.log(JSON.stringify(windows, null, 2))" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@075024469873.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01XPUzbsmwFUA7JT4PWhiaWB", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"find /root /home -name '*.pptx' -mmin -10000 2>/dev/null; echo '---'; ls -la ~/Desktop 2>/dev/null; echo '---'; find / -name 'stanford-nlp-seminar-talk-v2.pptx' 2>/dev/null\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Locating the presentation file on disk" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@075038011085.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01YL8RrEAtZfjVADxaYDS5pC", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"python3 -c 'import pptx; print(pptx.__version__)' 2>&1; echo '---'; pip3 list 2>/dev/null | head -50\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Checking for python-pptx availability" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@075050149301.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01DBGsoL8uUizHsPjYbLraQz", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"pip3 install python-pptx 2>&1 | tail -5\", timeout: 120 })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)", + "title": "Installing python-pptx library" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@075103108477.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_011tSjRVxf4drd5mGPX1zrgz", + "name": "execute", + "input": { + "code": "var script = `\nimport copy\nfrom pptx import Presentation\nfrom pptx.util import Emu, Pt\n\npath = \"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\"\nprs = Presentation(path)\nprint(\"SLIDE SIZE:\", prs.slide_width, prs.slide_height, prs.slide_width/914400, prs.slide_height/914400)\nprint(\"NUM SLIDES:\", len(prs.slides))\nprint()\nfor i, slide in enumerate(prs.slides):\n print(\"=\"*70)\n print(\"SLIDE\", i+1, \"| layout:\", slide.slide_layout.name, \"| layout_idx:\", prs.slide_layouts.index(slide.slide_layout) if slide.slide_layout in list(prs.slide_layouts) else \"?\")\n try:\n print(\" master:\", slide.slide_layout.slide_master.name)\n except Exception as e:\n print(\" master err\", e)\n for sh in slide.shapes:\n print(\" shape: name=%r type=%s ph=%s pos=(%s,%s) size=(%sx%s)\" % (\n sh.name, sh.shape_type,\n (sh.placeholder_format.idx, sh.placeholder_format.type) if sh.is_placeholder else None,\n sh.left, sh.top, sh.width, sh.height))\n`;\nawait machine.writeToFile({ text: script, path: \"/tmp/analyze1.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp && python3 analyze1.py 2>&1\", timeout: 120 })\nconsole.log(r.stdout)", + "title": "Analyzing slide layouts and shape positions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@075119125646.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_01HNg8P1Yw1SN4wyxLTdoaPp", + "name": "execute", + "input": { + "code": "var script2 = `\nfrom pptx import Presentation\npath = \"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\"\nprs = Presentation(path)\n\ndef dump(sh, indent=\" \"):\n if sh.shape_type == 6: # group\n for s in sh.shapes:\n dump(s, indent+\" \")\n return\n if sh.has_text_frame:\n txt = sh.text_frame.text\n if txt.strip():\n print(indent+\"[%s] %r\" % (sh.name, txt[:300]))\n\nfor i, slide in enumerate(prs.slides):\n print(\"=\"*70)\n print(\"SLIDE\", i+1)\n for sh in slide.shapes:\n dump(sh)\n`;\nawait machine.writeToFile({ text: script2, path: \"/tmp/analyze2.py\", overwrite: true })\nvar r2 = await machine.exec({ command: \"cd /tmp && python3 analyze2.py 2>&1\", timeout: 120 })\nconsole.log(r2.stdout)", + "title": "Extracting text from every slide" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@075134139044.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_0126on1iz2aLpxkYUct9XNYs", + "name": "execute", + "input": { + "code": "var script3 = `\nfrom pptx import Presentation\nfrom pptx.util import Emu, Pt\npath = \"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\"\nprs = Presentation(path)\n\nlayouts_by_master = {}\nfor m_i, master in enumerate(prs.slide_masters):\n print(\"MASTER %d: %r\" % (m_i, master.name))\n for l_i, lay in enumerate(master.slide_layouts):\n print(\" layout %d: %r\" % (l_i, lay.name))\nprint()\nprint(\"#\"*70)\nfor i, slide in enumerate(prs.slides):\n lay = slide.slide_layout\n mi = list(prs.slide_masters).index(lay.slide_master)\n li = list(lay.slide_master.slide_layouts).index(lay)\n print(\"SLIDE %2d -> master %d (%r) layout %d (%r)\" % (i+1, mi, lay.slide_master.name, li, lay.name))\n`;\nawait machine.writeToFile({ text: script3, path: \"/tmp/analyze3.py\", overwrite: true })\nvar r3 = await machine.exec({ command: \"cd /tmp && python3 analyze3.py 2>&1\", timeout: 120 })\nconsole.log(r3.stdout)", + "title": "Comparing slide masters and layouts used" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@075152405441.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01F8YAZPCCcJkMhU8JWGLuFp", + "name": "execute", + "input": { + "code": "var r4 = await machine.exec({ command: \"mkdir -p /tmp/render && cp /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx /tmp/render/orig.pptx && cd /tmp/render && timeout 300 soffice --headless -env:UserInstallation=file:///tmp/lo_profile1 --convert-to pdf orig.pptx 2>&1 | tail -3; ls -la /tmp/render\", timeout: 320 })\nconsole.log(r4.stdout)\nconsole.log(\"ERR:\", r4.stderr)", + "title": "Rendering the presentation to PDF for visual review" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@075215174237.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_01SUaoe49iyUXeTZMP1z4diz", + "name": "execute", + "input": { + "code": "var r5 = await machine.exec({ command: \"cd /tmp/render && (which pdftoppm convert gs); pdftoppm -png -r 50 orig.pdf pg 2>&1 | tail -3; ls /tmp/render | head -40\", timeout: 300 })\nconsole.log(r5.stdout)\nconsole.log(\"ERR:\", r5.stderr)", + "title": "Converting PDF pages to images" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@075227085847.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_013kK8BQfwJDRYorfqzw72Ux", + "name": "execute", + "input": { + "code": "var sheet = `\nfrom PIL import Image\nimport glob, os\nfiles = sorted(glob.glob('/tmp/render/pg-*.png'))\nprint(len(files))\nims = [Image.open(f) for f in files]\nw,h = ims[0].size\nprint(\"tile\", w, h)\ndef sheet(imgs, labels, cols, out):\n rows = (len(imgs)+cols-1)//cols\n canvas = Image.new('RGB', (cols*(w+6), rows*(h+18)), 'white')\n from PIL import ImageDraw\n d = ImageDraw.Draw(canvas)\n for i,(im,lb) in enumerate(zip(imgs,labels)):\n r,c = divmod(i, cols)\n x,y = c*(w+6), r*(h+18)\n d.text((x+2,y+3), lb, fill='red')\n canvas.paste(im, (x, y+16))\n canvas.save(out)\n print(out, canvas.size)\n# old slides 1-17\nsheet(ims[0:17], [\"S%d\"%(i+1) for i in range(17)], 5, '/tmp/render/old_1_17.png')\n# new slides 18-24\nsheet(ims[17:24], [\"S%d\"%(i+18) for i in range(7)], 4, '/tmp/render/new_18_24.png')\n`;\nawait machine.writeToFile({ text: sheet, path: \"/tmp/sheet.py\", overwrite: true })\nvar r6 = await machine.exec({ command: \"cd /tmp && python3 sheet.py 2>&1\", timeout: 180 })\nconsole.log(r6.stdout)", + "title": "Building comparison contact sheets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@075245088681.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01EGR9xVrmJ3Jce84u9JA6T4", + "name": "execute", + "input": { + "code": "var sheet2 = `\nfrom PIL import Image, ImageDraw\nimport glob\nfiles = sorted(glob.glob('/tmp/render/pg-*.png'))\nims = [Image.open(f) for f in files]\nw,h = ims[0].size\ndef sheet(idxs, cols, out):\n imgs=[ims[i] for i in idxs]\n rows = (len(imgs)+cols-1)//cols\n canvas = Image.new('RGB', (cols*(w+6), rows*(h+18)), 'white')\n d = ImageDraw.Draw(canvas)\n for k,i in enumerate(idxs):\n r,c = divmod(k, cols)\n x,y = c*(w+6), r*(h+18)\n d.text((x+2,y+3), \"S%d\"%(i+1), fill='red')\n canvas.paste(ims[i], (x, y+16))\n canvas.save(out); print(out, canvas.size)\nsheet(range(0,6), 3, '/tmp/render/oldA.png')\nsheet(range(6,12), 3, '/tmp/render/oldB.png')\nsheet(range(12,17), 3, '/tmp/render/oldC.png')\n`;\nawait machine.writeToFile({ text: sheet2, path: \"/tmp/sheet2.py\", overwrite: true })\nvar r7 = await machine.exec({ command: \"cd /tmp && python3 sheet2.py 2>&1\", timeout: 180 })\nconsole.log(r7.stdout)", + "title": "Splitting the existing slides into smaller sheets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@075313088144.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_01M29h5BevhX3q5HcHnrbyzP", + "name": "execute", + "input": { + "code": "var r8 = await machine.exec({ command: \"cd /tmp/render && pdftoppm -png -r 100 -f 18 -l 24 orig.pdf hi 2>&1 | tail -2; ls /tmp/render/hi*\", timeout: 300 })\nconsole.log(r8.stdout)", + "title": "Rendering the new slides at higher resolution" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@075331089879.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01U6uML1dvv72xP9ifYEj4KH", + "name": "execute", + "input": { + "code": "var r9 = await machine.exec({ command: \"cd /tmp/render && pdftoppm -png -r 100 -f 4 -l 7 orig.pdf oldhi 2>&1 | tail -2; ls /tmp/render/oldhi*\", timeout: 300 })\nconsole.log(r9.stdout)", + "title": "Rendering reference old slides at high resolution" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@075355855718.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01Dpi1x9Xb1u2oyEjz5peF9V", + "name": "execute", + "input": { + "code": "var script4 = `\nfrom pptx import Presentation\nfrom pptx.util import Emu, Pt\npath = \"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\"\nprs = Presentation(path)\n\ndef describe(slide, n):\n print(\"=\"*80)\n print(\"SLIDE\", n)\n for sh in slide.shapes:\n print(\" SHAPE name=%r type=%s ph=%s L=%s T=%s W=%s H=%s\" % (\n sh.name, sh.shape_type,\n (sh.placeholder_format.idx, str(sh.placeholder_format.type)) if sh.is_placeholder else None,\n sh.left, sh.top, sh.width, sh.height))\n if not sh.has_text_frame: continue\n tf = sh.text_frame\n bp = tf._bodyPr\n print(\" bodyPr:\", bp.get('anchor'), \"autofit:\", [c.tag.split('}')[1] for c in bp], \"insets:\", bp.get('lIns'), bp.get('tIns'), bp.get('rIns'), bp.get('bIns'))\n for pi, p in enumerate(tf.paragraphs):\n pPr = p._pPr\n marL = pPr.get('marL') if pPr is not None else None\n indent = pPr.get('indent') if pPr is not None else None\n lvl = p.level\n bullet = None\n if pPr is not None:\n for c in pPr:\n t = c.tag.split('}')[1]\n if t in ('buChar','buNone','buAutoNum'):\n bullet = (t, c.get('char'), c.get('typeface'))\n runs = []\n for r in p.runs:\n f = r.font\n col = None\n try:\n if f.color and f.color.type is not None:\n col = str(f.color.type)+\":\"+str(f.color.rgb if f.color.type==1 else f.color.theme_color)\n except Exception as e: col = \"err\"\n runs.append((r.text[:60], f.name, f.size.pt if f.size else None, f.bold, f.italic, col))\n if not runs and not p.text.strip(): \n print(\" p%d lvl=%d align=%s marL=%s indent=%s bullet=%s (EMPTY)\" % (pi,lvl,p.alignment,marL,indent,bullet))\n continue\n print(\" p%d lvl=%d align=%s marL=%s indent=%s bullet=%s lnSpc=%s\" % (pi,lvl,p.alignment,marL,indent,bullet, pPr.find('{http://schemas.openxmlformats.org/drawingml/2006/main}lnSpc') is not None if pPr is not None else None))\n for rr in runs: print(\" run:\", rr)\n\nfor n in [2,3,4,5,6,7,16,17]:\n describe(prs.slides[n-1], n)\n`;\nawait machine.writeToFile({ text: script4, path: \"/tmp/analyze4.py\", overwrite: true })\nvar r10 = await machine.exec({ command: \"cd /tmp && python3 analyze4.py > /tmp/old_fmt.txt 2>&1; wc -l /tmp/old_fmt.txt; head -120 /tmp/old_fmt.txt\", timeout: 180 })\nconsole.log(r10.stdout)", + "title": "Extracting detailed formatting from existing slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@075422749894.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01VTP6958hRe9T5xKZ3P8L9L", + "name": "execute", + "input": { + "code": "var r11 = await machine.exec({ command: \"sed -n '120,198p' /tmp/old_fmt.txt\", timeout: 60 })\nconsole.log(r11.stdout)", + "title": "Reviewing formatting of remaining old slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@075442669731.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01868V7675VBUXKso98QPFK9", + "name": "execute", + "input": { + "code": "var script5 = script4.replace(\"for n in [2,3,4,5,6,7,16,17]:\", \"for n in [1,2,18,19,20,21,22,23,24]:\")\nawait machine.writeToFile({ text: script5, path: \"/tmp/analyze5.py\", overwrite: true })\nvar r12 = await machine.exec({ command: \"cd /tmp && python3 analyze5.py > /tmp/new_fmt.txt 2>&1; wc -l /tmp/new_fmt.txt; sed -n '1,70p' /tmp/new_fmt.txt\", timeout: 180 })\nconsole.log(r12.stdout)", + "title": "Extracting detailed formatting from the new slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@075458943161.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_017je9P8ze3xvPSJczzvJDfK", + "name": "execute", + "input": { + "code": "var r13 = await machine.exec({ command: \"sed -n '70,144p' /tmp/new_fmt.txt\", timeout: 60 })\nconsole.log(r13.stdout)", + "title": "Reviewing formatting of new slides 21-24" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@075519188751.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01Fmm8QwFL7icTH4fxU9TrNT", + "name": "execute", + "input": { + "code": "var r14 = await machine.exec({ command: \"sed -n '1,45p' /tmp/new_fmt.txt\", timeout: 60 })\nconsole.log(r14.stdout)", + "title": "Reviewing title slide and section divider formatting" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@075531329564.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_01H3viHd8NZAFgAmZ5Wd2XgG", + "name": "execute", + "input": { + "code": "var styleSpec = {\n oldFont: \"Gill Sans\",\n newFont: \"Manrope\",\n newSlides: [18,19,20,21,22,23,24],\n contentTitle: { L:306360, T:128160, W:8494200, H:639000, size:30, align:\"CENTER\", color:\"000000\",\n anchor:\"t\", insets:[90000,91440,90000,91440] },\n sectionDivider: { L:324360, T:2048400, W:8494200, H:727560, size:36, align:\"CENTER\", color:\"000000\" },\n body: { size:18, color:\"000000\", bulletChar:\"●\", marL:457200, indent:-342720 },\n refs: { size:6, color:\"0000FF\" },\n slideNumPh: { L:8746920, T:4866120, W:338400, H:188640, anchor:\"b\", insets:19080, align:\"CENTER\" },\n newProblems: [\"font Manrope->Gill Sans\",\"body color 595959->000000\",\"title 25pt left -> 30pt centered + reposition\",\n \"divider font + geometry\",\"refs 7pt black -> 6pt blue 0000FF\",\"missing slide-number placeholder\",\n \"slides use masters 3&4 instead of master 0\"]\n}\nvar census = `\nfrom pptx import Presentation\nfrom collections import Counter\nprs = Presentation(\"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\")\ndef walk(sh):\n if sh.shape_type == 6:\n for s in sh.shapes: yield from walk(s)\n return\n yield sh\nfor grp, rng in [(\"OLD 1-17\", range(0,17)), (\"NEW 18-24\", range(17,24))]:\n fonts=Counter(); sizes=Counter(); colors=Counter(); combos=Counter()\n for i in rng:\n for sh in prs.slides[i].shapes:\n for s in walk(sh):\n if not s.has_text_frame: continue\n for p in s.text_frame.paragraphs:\n for r in p.runs:\n if not r.text.strip(): continue\n f=r.font\n col=None\n try:\n if f.color and f.color.type is not None and f.color.type==1: col=str(f.color.rgb)\n except: col='err'\n fonts[f.name]+=1; sizes[f.size.pt if f.size else None]+=1; colors[col]+=1\n combos[(f.name, f.size.pt if f.size else None, col)]+=1\n print(\"###\", grp)\n print(\" fonts:\", dict(fonts))\n print(\" sizes:\", dict(sorted(sizes.items(), key=lambda x:(x[0] is None, x[0]))))\n print(\" colors:\", dict(colors))\n print(\" combos:\")\n for k,v in sorted(combos.items(), key=lambda x:-x[1]): print(\" \", k, v)\n`;\nawait machine.writeToFile({ text: census, path: \"/tmp/census.py\", overwrite: true })\nvar r15 = await machine.exec({ command: \"cd /tmp && python3 census.py 2>&1\", timeout: 180 })\nconsole.log(r15.stdout)", + "title": "Running a font, size and color census on the deck" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@075602329321.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_019LcFs8uxhFUMPT3PpKypsu", + "name": "execute", + "input": { + "code": "var r16 = await machine.exec({ command: `cd /tmp && rm -rf unz && mkdir unz && cd unz && unzip -o -q ../render/orig.pptx && ls ppt/slides/ && echo '=== REL MAP ===' && for n in 17 18 19 20 21 22 23 24; do echo -n \"slide$n -> \"; grep -o 'slideLayout[0-9]*\\\\.xml' ppt/slides/_rels/slide$n.xml.rels | head -1; done` , timeout: 180 })\nconsole.log(r16.stdout)", + "title": "Unpacking the file and mapping slides to layouts" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@075633546157.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01EbKUzPubeGojf1Lz6o7nTm", + "name": "execute", + "input": { + "code": "var r17 = await machine.exec({ command: `cd /tmp/unz && python3 -c \"\nimport re\nfor f in ['ppt/slides/slide5.xml','ppt/slides/slide19.xml']:\n d=open(f).read()\n print('#'*30, f, 'len', len(d))\n print(d[:2600])\n print()\n\"`, timeout: 120 })\nconsole.log(r17.stdout)", + "title": "Comparing raw XML of an old and a new slide" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@075646132463.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_015DaVybJHM8PwiJ3PzrATQJ", + "name": "execute", + "input": { + "code": "var r18 = await machine.exec({ command: `cd /tmp/unz && python3 -c \"\nimport re,glob\nprint('=== OLD buFont/buClr ===')\ns=set()\nfor n in range(2,18):\n d=open('ppt/slides/slide%d.xml'%n).read()\n for m in re.findall(r'.*?||', d): s.add(m)\nfor x in sorted(s): print(' ', x)\nprint('=== NEW buFont/buClr ===')\ns2=set()\nfor n in range(18,25):\n d=open('ppt/slides/slide%d.xml'%n).read()\n for m in re.findall(r'.*?||', d): s2.add(m)\nfor x in sorted(s2): print(' ', x)\nprint()\nprint('=== OLD slide-number placeholder (slide5 tail) ===')\nd=open('ppt/slides/slide5.xml').read()\ni=d.find('PlaceHolder')\nprint(d[i-400:i+1200])\n\"`, timeout: 120 })\nconsole.log(r18.stdout)", + "title": "Checking bullet styling and slide number placeholder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@075711385980.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_0174LCejg4bV73Na1C97DYa7", + "name": "execute", + "input": { + "code": "var chk = `\nimport re\nfor n in range(18,25):\n d=open('/tmp/unz/ppt/slides/slide%d.xml'%n).read()\n print('### slide', n, 'len', len(d))\n # all occurrences of 595959 with 120 chars of preceding context\n for m in re.finditer(r'595959', d):\n pre = d[max(0,m.start()-110):m.start()]\n print(' 595959 ctx:', pre[-100:].replace(chr(10),''))\n print(' --- sz=700 runs ---')\n for m in re.finditer(r'sz=\"700\"', d):\n print(' ', d[m.start()-60:m.start()+300].replace(chr(10),''))\n print(' --- lstStyle defRPr ---')\n for m in re.finditer(r']*>.{0,200}?', d, re.S):\n print(' ', m.group()[:250])\n print(' --- hlink ---', len(re.findall('hlinkClick', d)))\n print()\n`;\nawait machine.writeToFile({ text: chk, path: \"/tmp/chk.py\", overwrite: true })\nvar rc = await machine.exec({ command: \"cd /tmp && python3 chk.py > /tmp/chk.txt 2>&1; wc -l /tmp/chk.txt; grep -c '595959 ctx' /tmp/chk.txt; grep -o 'ctx:.*' /tmp/chk.txt | sed 's/.*\\\\(]*>\\\\)[^<]*$/PRE:\\\\1/' | sort | uniq -c | sort -rn | head -20\", timeout: 120 })\nconsole.log(rc.stdout)", + "title": "Verifying where the gray color is used in new slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@075743754751.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01Gh9uEqP5mLYNUrHA4gf69p", + "name": "execute", + "input": { + "code": "var r19 = await machine.exec({ command: `cd /tmp/unz && python3 -c \"\nimport re\ns=set()\nfor n in range(2,18):\n d=open('ppt/slides/slide%d.xml'%n).read()\n for m in re.findall(r'', d): s.add(m)\n for m in re.findall(r'', d): s.add(m)\nprint('OLD bullet styling:'); [print(' ',x) for x in sorted(s)]\nprint()\nprint('OLD 6pt reference runs (sample):')\nc=0\nfor n in range(2,18):\n d=open('ppt/slides/slide%d.xml'%n).read()\n for m in re.finditer(r'sz=\\\\\"600\\\\\"', d):\n print(' s%d:'%n, d[m.start()-80:m.start()+330].replace(chr(10),''))\n c+=1\n if c>=3: break\n if c>=3: break\n\"`, timeout: 120 })\nconsole.log(r19.stdout)", + "title": "Checking old deck bullet color and reference text style" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@075759898516.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_015jUD3nP7wrUS6DoYZhTdRZ", + "name": "execute", + "input": { + "code": "var r20 = await machine.exec({ command: \"grep -A2 'sz=700 runs' /tmp/chk.txt | head -40; echo '======'; grep -n 'sz=\\\"700\\\"' /tmp/chk.txt | head; echo '=== raw ==='; cd /tmp/unz && python3 -c \\\"\\nimport re\\nfor n in range(18,25):\\n d=open('ppt/slides/slide%d.xml'%n).read()\\n for m in re.finditer(r'sz=.700.', d):\\n print('s%d:'%n, d[m.start()-120:m.start()+360].replace(chr(10),''))\\n print()\\n\\\"\", timeout: 120 })\nconsole.log(r20.stdout)", + "title": "Inspecting reference text formatting on new slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@075812992129.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_01WiYPJc5mGmWhACrrXgn91f", + "name": "execute", + "input": { + "code": "var ext = `\nimport re\nd=open('/tmp/unz/ppt/slides/slide5.xml').read()\n# find all blocks, print the one containing 'sldNum'\nsps=[]\ni=0\nwhile True:\n a=d.find('', i)\n if a<0: break\n b=d.find('', a)\n sps.append(d[a:b+7]); i=b+7\nprint(\"num sp:\", len(sps))\nfor s in sps:\n if 'sldNum' in s or 'fld' in s:\n print(\"-----\")\n print(s)\nopen('/tmp/sldnum_template.xml','w').write([s for s in sps if 'sldNum' in s][0])\nprint()\nprint(\"=== spTree tail of slide19 ===\")\nd19=open('/tmp/unz/ppt/slides/slide19.xml').read()\nprint(d19[-500:])\nprint()\nprint(\"=== max shape ids per new slide ===\")\nfor n in range(18,25):\n dd=open('/tmp/unz/ppt/slides/slide%d.xml'%n).read()\n ids=[int(x) for x in re.findall(r'&1\", timeout: 120 })\nconsole.log(re1.stdout)", + "title": "Extracting the slide number placeholder template" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@075852350929.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01AGqKxzPGTaAAkNisNh53m8", + "name": "execute", + "input": { + "code": "var chk2 = `\nimport re\n# 1. OLD reference run rPr full attrs\nprint(\"=== OLD ref rPr (sz=600) samples ===\")\nseen=set()\nfor n in range(2,18):\n d=open('/tmp/unz/ppt/slides/slide%d.xml'%n).read()\n for m in re.finditer(r']*sz=\"600\"[^>]*>.*?', d, re.S):\n g=m.group()\n key=re.sub(r'r:id=\"[^\"]*\"','r:id=\"X\"',g)\n if key not in seen:\n seen.add(key); print(\" s%d: %s\"%(n,g))\n if len(seen)>=4: break\nprint()\nprint(\"=== OLD ref shape (whole sp from slide5) ===\")\nd=open('/tmp/unz/ppt/slides/slide5.xml').read()\ni=0\nwhile True:\n a=d.find('',i)\n if a<0: break\n b=d.find('',a); sp=d[a:b+7]; i=b+7\n if 'sz=\"600\"' in sp:\n print(sp[:1500]); break\nprint()\nprint(\"=== OLD divider title sp (slide2) ===\")\nd2=open('/tmp/unz/ppt/slides/slide2.xml').read()\ni=0\nwhile True:\n a=d2.find('',i)\n if a<0: break\n b=d2.find('',a); sp=d2[a:b+7]; i=b+7\n if 'sz=\"3600\"' in sp:\n print(sp[:1400]); break\nprint()\nprint(\"=== layouts 37/49: sldNum placeholder? ===\")\nfor L in (37,49,25):\n dl=open('/tmp/unz/ppt/slideLayouts/slideLayout%d.xml'%L).read()\n print(\" layout%d:\"%L, re.findall(r'', dl), \"| name=\", re.findall(r'name=\"([^\"]*)\"', dl)[:1])\nprint()\nprint(\"=== NEW slides: hlinkClick count & u= attr ===\")\nfor n in range(18,25):\n dd=open('/tmp/unz/ppt/slides/slide%d.xml'%n).read()\n print(\" s%d hlink=%d u_attr=%s sz700=%d\"%(n, dd.count('hlinkClick'), set(re.findall(r'u=\"[^\"]*\"', dd)), dd.count('sz=\"700\"')))\n`;\nawait machine.writeToFile({ text: chk2, path: \"/tmp/chk2.py\", overwrite: true })\nvar rk2 = await machine.exec({ command: \"cd /tmp && python3 chk2.py 2>&1 | head -60\", timeout: 120 })\nconsole.log(rk2.stdout)", + "title": "Confirming reference styling and layout placeholder details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@075930435794.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01Snt38WsS7vk59i3Fmf1yoL", + "name": "execute", + "input": { + "code": "var transform = String.raw`\nimport re, os, shutil, zipfile, sys\n\nSRC = \"/tmp/render/orig.pptx\"\nWORK = \"/tmp/work\"\nOUT = \"/tmp/out.pptx\"\n\nif os.path.exists(WORK): shutil.rmtree(WORK)\nos.makedirs(WORK)\nwith zipfile.ZipFile(SRC) as z:\n names = z.namelist()\n z.extractall(WORK)\n\nOLD_FONT = \"Gill Sans\"\n\nTITLE_GEOM = ('306360','128160','8494200','639000') # content title (old deck)\nDIVIDER_GEOM = ('324360','2048400','8494200','727560') # section divider (old deck, slide2)\nBODYPR = ''\n\nSLDNUM = (''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n '1'\n ''\n ''\n '')\n\nSLDNUM_IDX = {18: '4', 19: '5', 20: '5', 21: '5', 22: '5', 23: '5', 24: '5'}\n\ndef split_sps(d):\n \"\"\"Return list of (start, end) for each top-level-ish block.\"\"\"\n out = []; i = 0\n while True:\n a = d.find('', i)\n if a < 0: break\n b = d.find('', a)\n if b < 0: break\n out.append((a, b + 7)); i = b + 7\n return out\n\ndef fix_title_sp(sp, geom, old_sz, new_sz):\n # geometry\n sp = re.sub(r'', '' % (geom[0], geom[1]), sp, count=1)\n sp = re.sub(r'', '' % (geom[2], geom[3]), sp, count=1)\n # bodyPr -> old deck's\n sp = re.sub(r']*?/>', BODYPR, sp, count=1)\n sp = re.sub(r'', BODYPR, sp, count=1, flags=re.S)\n # centre every paragraph\n sp = re.sub(r']*algn=)([^>]*?)/>', r'', sp)\n sp = re.sub(r']*algn=)([^>]*?)>', r'', sp)\n if '', '')\n # font size\n if new_sz:\n sp = sp.replace('sz=\"%s\"' % old_sz, 'sz=\"%s\"' % new_sz)\n return sp\n\nreport = {}\nfor n in range(18, 25):\n p = os.path.join(WORK, 'ppt/slides/slide%d.xml' % n)\n d = open(p, encoding='utf-8').read()\n orig = d\n ch = []\n\n # ---- 1. font family: Manrope* -> Gill Sans ----\n d, c1 = re.subn(r'typeface=\"Manrope[^\"]*\"', 'typeface=\"%s\"' % OLD_FONT, d)\n if c1: ch.append('font x%d' % c1)\n\n # ---- 2. body/bullet grey 595959 -> black 000000 ----\n d, c2 = re.subn(r'val=\"595959\"', 'val=\"000000\"', d, flags=re.I)\n if c2: ch.append('grey->black x%d' % c2)\n\n # ---- 3. title placeholder geometry / size / centring ----\n sps = split_sps(d)\n newd = []; last = 0; tfix = 0\n for (a, b) in sps:\n sp = d[a:b]\n if ' 6pt blue underlined (deck convention) ----\n if 'sz=\"700\"' in d:\n def cite(m):\n t = m.group(0)\n t = t.replace('sz=\"700\"', 'sz=\"600\"')\n t = re.sub(r'val=\"000000\"', 'val=\"0000ff\"', t)\n if 'u=\"sng\"' not in t:\n t = t.replace(']*sz=\"700\"[^>]*>(?:.*?)?', cite, d, flags=re.S)\n d = d.replace('sz=\"700\"', 'sz=\"600\"')\n ch.append('citations x%d' % c4)\n\n # ---- 5. slide-number placeholder ----\n if 'type=\"sldNum\"' not in d:\n ids = [int(x) for x in re.findall(r'', SLDNUM.format(id=nid, idx=SLDNUM_IDX[n], fid='{1D2B5A3E-4C6F-4A1B-9E8D-%012d}' % n) + '')\n ch.append('sldNum added')\n\n open(p, 'w', encoding='utf-8').write(d)\n report[n] = ch\n print('slide %d: %s (%d -> %d bytes)' % (n, ', '.join(ch), len(orig), len(d)))\n\n# ---- repackage ----\nif os.path.exists(OUT): os.remove(OUT)\nzf = zipfile.ZipFile(OUT, 'w', zipfile.ZIP_DEFLATED)\nfor name in names:\n zf.write(os.path.join(WORK, name), name)\nzf.close()\nprint('WROTE', OUT, os.path.getsize(OUT))\n`;\nawait machine.writeToFile({ text: transform, path: \"/tmp/transform.py\", overwrite: true })\nvar rt = await machine.exec({ command: \"cd /tmp && python3 transform.py 2>&1\", timeout: 300 })\nconsole.log(rt.stdout)\nconsole.log(\"ERR:\", rt.stderr)", + "title": "Applying style fixes to the new slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@080033957478.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01X7GxjY2ZLhrADy96epSYU2", + "name": "execute", + "input": { + "code": "var verify = census.replace('/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx', '/tmp/out.pptx')\nawait machine.writeToFile({ text: verify, path: \"/tmp/verify.py\", overwrite: true })\nvar rv = await machine.exec({ command: \"cd /tmp && python3 verify.py 2>&1\", timeout: 180 })\nconsole.log(rv.stdout)", + "title": "Verifying fonts, sizes and colors now match" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@080047442174.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01GEs6nsUdfJ1NaARyHS6eCZ", + "name": "execute", + "input": { + "code": "var rr = await machine.exec({ command: \"mkdir -p /tmp/render2 && cp /tmp/out.pptx /tmp/render2/ && cd /tmp/render2 && timeout 300 soffice --headless -env:UserInstallation=file:///tmp/lo_profile2 --convert-to pdf out.pptx 2>&1 | tail -2 && pdftoppm -png -r 100 -f 18 -l 24 out.pdf new 2>&1 | tail -2; pdftoppm -png -r 50 out.pdf all 2>&1|tail -1; ls /tmp/render2 | head\", timeout: 400 })\nconsole.log(rr.stdout)", + "title": "Rendering the corrected presentation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@080110734883.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01344NyFjgaBbkC5w1qCfdgv", + "name": "execute", + "input": { + "code": "var sheet3 = `\nfrom PIL import Image, ImageDraw\nimport glob\nfiles = sorted(glob.glob('/tmp/render2/new-*.png'))\nims=[Image.open(f) for f in files]\nw,h=ims[0].size\ndef mk(idxs, cols, out, labels):\n rows=(len(idxs)+cols-1)//cols\n c=Image.new('RGB',(cols*(w+6), rows*(h+18)),'white'); d=ImageDraw.Draw(c)\n for k,i in enumerate(idxs):\n r,cc=divmod(k,cols); x,y=cc*(w+6), r*(h+18)\n d.text((x+2,y+3), labels[k], fill='red'); c.paste(ims[i],(x,y+16))\n c.resize((c.size[0]//2, c.size[1]//2)).save(out); print(out)\nmk([0,1,2], 3, '/tmp/render2/A.png', ['S18','S19','S20'])\nmk([3,4,5,6], 2, '/tmp/render2/B.png', ['S21','S22','S23','S24'])\n`;\nawait machine.writeToFile({ text: sheet3, path: \"/tmp/sheet3.py\", overwrite: true })\nvar rs3 = await machine.exec({ command: \"cd /tmp && python3 sheet3.py 2>&1\", timeout: 180 })\nconsole.log(rs3.stdout)", + "title": "Building review sheets of the restyled slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@080133738706.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01XLsWvhTbYfXVYtWpAVvGCz", + "name": "execute", + "input": { + "code": "var cmp = String.raw`\nfrom PIL import Image\na = Image.open('/tmp/render/pg-02.png') # old divider (slide 2)\nb = Image.open('/tmp/render2/all-18.png') # new divider (slide 18)\nw,h = a.size\nc = Image.new('RGB',(w, h*2+8),'white')\nc.paste(a,(0,0)); c.paste(b,(0,h+8)); c.save('/tmp/div_cmp.png')\nprint('div_cmp', c.size, '(top=old S2, bottom=new S18)')\n\nimport re\nfrom pptx import Presentation\nfor tag, path, rng in [('OLD','/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx',range(0,17)),\n ('NEW','/tmp/out.pptx',range(17,24))]:\n prs=Presentation(path); seen={}\n for i in rng:\n for sh in prs.slides[i].shapes:\n if not sh.has_text_frame: continue\n for p in sh.text_frame.paragraphs:\n if not p.text.strip(): continue\n pPr=p._pPr; bu=None\n if pPr is not None:\n for ch in pPr:\n t=ch.tag.split('}')[1]\n if t in ('buChar','buNone'): bu=(t, ch.get('char'))\n seen.setdefault(p.level, set()).add(bu)\n print(tag, 'bullets by level:', {k:sorted(str(x) for x in v) for k,v in sorted(seen.items())})\n`;\nawait machine.writeToFile({ text: cmp, path: \"/tmp/cmp.py\", overwrite: true })\nvar rcm = await machine.exec({ command: \"cd /tmp && python3 cmp.py 2>&1\", timeout: 180 })\nconsole.log(rcm.stdout)", + "title": "Comparing dividers and bullet characters by level" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@080227789945.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_011eJhiPb7NkvrmfC1Bf5ADB", + "name": "execute", + "input": { + "code": "var cmp2 = String.raw`\nfrom PIL import Image, ImageDraw\na = Image.open('/tmp/render/pg-05.png') # old content slide\nb = Image.open('/tmp/render2/all-20.png') # new content slide\nw,h=a.size\nc=Image.new('RGB',(w,h*2+8),'white')\nc.paste(a,(0,0)); c.paste(b,(0,h+8))\nd=ImageDraw.Draw(c)\n# guide lines at title band (old geom: y 128160..767160 of 5143500)\nfor base in (0, h+8):\n y1 = base + int(h*128160/5143500); y2 = base + int(h*767160/5143500)\n d.line([(0,y1),(w,y1)], fill='red'); d.line([(0,y2),(w,y2)], fill='red')\n d.line([(w//2,base),(w//2,base+h)], fill=(0,180,0))\nc.save('/tmp/content_cmp.png'); print(c.size)\n`;\nawait machine.writeToFile({ text: cmp2, path: \"/tmp/cmp2.py\", overwrite: true })\nvar rcm2 = await machine.exec({ command: \"cd /tmp && python3 cmp2.py 2>&1\", timeout: 180 })\nconsole.log(rcm2.stdout)", + "title": "Overlaying title guides on old and new content slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@080248542501.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_01HCpTU7gr4257siS6UU7mYY", + "name": "execute", + "input": { + "code": "var app = await machine.getAppWindow(\"stanford-nlp-seminar-talk-v2.pptx - LibreOffice Impress\")\nawait app.activate()\nawait wait({ waitTime: 1 })\nvar sc = await app.screenshot()\nconsole.log(\"screenshot taken\")", + "title": "Bringing the Impress window into focus" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@080312022934.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_01QPp8Pjd4Qd5Y4ZLLVd97et", + "name": "execute", + "input": { + "code": "await app.press({ key: 'ctrl+w' })\nawait wait({ waitTime: 3 })\nvar w2 = await machine.listAppWindows()\nconsole.log(JSON.stringify(w2, null, 2))\nvar lk = await machine.exec({ command: \"ls -la /home/user/Desktop/ ; pgrep -a soffice | head\" })\nconsole.log(lk.stdout)", + "title": "Closing the document in Impress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@080337001377.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_01Faoxknf9Wc9YYuMfVvXXxo", + "name": "execute", + "input": { + "code": "var ap = await machine.exec({ command: \"cp /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx /tmp/backup_orig.pptx && cat /tmp/out.pptx > /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx && ls -la /home/user/Desktop/ && echo '--- integrity ---' && python3 -c \\\"from pptx import Presentation; p=Presentation('/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx'); print('slides:', len(p.slides))\\\"\", timeout: 180 })\nconsole.log(ap.stdout)\nconsole.log(\"ERR:\", ap.stderr)", + "title": "Saving the restyled deck over the original file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@080352117471.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_014qY28YdhrNzNeK8JxHy1Hq", + "name": "execute", + "input": { + "code": "await machine.open({ url: \"/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx\" })\nawait wait({ waitTime: 8 })\nvar w3 = await machine.listAppWindows()\nconsole.log(JSON.stringify(w3, null, 2))", + "title": "Reopening the presentation in Impress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@080403333989.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01TtFwGzxtWZBYKA4Rut39RW", + "name": "execute", + "input": { + "code": "var op = await machine.exec({ command: \"nohup soffice --impress /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx >/dev/null 2>&1 & sleep 12; pgrep -a soffice | head; ls -la /home/user/Desktop/\", timeout: 120 })\nconsole.log(op.stdout)\nvar w3 = await machine.listAppWindows()\nconsole.log(JSON.stringify(w3, null, 2))", + "title": "Opening the deck in LibreOffice Impress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@080428936651.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_018CtRdY8XeHQGSNFywgBULH", + "name": "execute", + "input": { + "code": "var app2 = await machine.getAppWindow(\"stanford-nlp-seminar-talk-v2.pptx - LibreOffice Impress\")\nawait app2.activate()\nawait wait({ waitTime: 2 })\nawait app2.screenshot()\nconsole.log(\"ok\")", + "title": "Viewing the reopened presentation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@080445266105.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_0151UEeRvCvJGbp8UZPjhzUS", + "name": "execute", + "input": { + "code": "var snapA = await app2.snapshot()\nvar hits = snapA.snapshot.grep(\"Slide Sorter|Outline|Normal\", { context: 0, max: 20 })\nconsole.log(hits)", + "title": "Locating the slide sorter view option" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@080503400171.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_015He487cFTdzdfViMvvFbKR", + "name": "execute", + "input": { + "code": "console.log(snapA.snapshot.read(830, 842))\nconsole.log(\"--- refs near view menu ---\")\nfor (const [k,v] of Object.entries(snapA.refs)) {\n if (v.name === 'Slide Sorter' || v.name === 'View') console.log(k, v.role, JSON.stringify(v.name), JSON.stringify(v.bounds))\n}", + "title": "Finding the Slide Sorter menu entry" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@080518786108.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_01KNw5yt3PvQ7HuDxqDcBGKb", + "name": "execute", + "input": { + "code": "var final = String.raw`\nimport zipfile, hashlib\nA = zipfile.ZipFile('/tmp/backup_orig.pptx')\nB = zipfile.ZipFile('/home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx')\nna, nb = set(A.namelist()), set(B.namelist())\nprint(\"parts only in orig:\", sorted(na-nb))\nprint(\"parts only in new :\", sorted(nb-na))\nchanged=[]\nfor n in sorted(na & nb):\n if hashlib.md5(A.read(n)).hexdigest() != hashlib.md5(B.read(n)).hexdigest():\n changed.append(n)\nprint(\"CHANGED PARTS (%d):\" % len(changed))\nfor c in changed: print(\" \", c)\n`;\nawait machine.writeToFile({ text: final, path: \"/tmp/final.py\", overwrite: true })\nvar rf = await machine.exec({ command: \"cd /tmp && python3 final.py 2>&1\", timeout: 180 })\nconsole.log(rf.stdout)", + "title": "Confirming only the new slides were modified" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@080537987030.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_01Fy7UbZWwk4jUpeCrJPKiRC", + "name": "execute", + "input": { + "code": "var fr = await machine.exec({ command: \"mkdir -p /tmp/final_r && cp /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx /tmp/final_r/f.pptx && cd /tmp/final_r && timeout 300 soffice --headless -env:UserInstallation=file:///tmp/lo_profile3 --convert-to pdf f.pptx 2>&1|tail -1 && pdftoppm -png -r 50 f.pdf p 2>&1|tail -1; ls /tmp/final_r/p-*.png | wc -l\", timeout: 400 })\nconsole.log(fr.stdout)", + "title": "Rendering the final saved file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@080600274435.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_015PueSKqqyQNsX5Q2w9RjDC", + "name": "execute", + "input": { + "code": "var finalSheet = String.raw`\nfrom PIL import Image, ImageDraw\nimport glob\nfiles = sorted(glob.glob('/tmp/final_r/p-*.png'))\nims=[Image.open(f) for f in files]\nw,h=ims[0].size\ndef mk(idxs, cols, out):\n rows=(len(idxs)+cols-1)//cols\n c=Image.new('RGB',(cols*(w+6), rows*(h+18)),'white'); d=ImageDraw.Draw(c)\n for k,i in enumerate(idxs):\n r,cc=divmod(k,cols); x,y=cc*(w+6), r*(h+18)\n col = 'red' if i>=17 else 'blue'\n d.text((x+2,y+3), \"S%d %s\"%(i+1, 'NEW' if i>=17 else 'old'), fill=col)\n c.paste(ims[i],(x,y+16))\n c.save(out); print(out, c.size)\nmk([13,14,15,16,17,18], 3, '/tmp/final_r/boundary.png')\nmk([18,19,20,21,22,23], 3, '/tmp/final_r/rest.png')\n`;\nawait machine.writeToFile({ text: finalSheet, path: \"/tmp/fs.py\", overwrite: true })\nvar rfs = await machine.exec({ command: \"cd /tmp && python3 fs.py 2>&1\", timeout: 180 })\nconsole.log(rfs.stdout)", + "title": "Building a final old-to-new boundary comparison" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@080616138355.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_016p4Kx9foRRpUH4M1eRUzaQ", + "name": "execute", + "input": { + "code": "var snapB = await app2.snapshot()\n// find the main document/slide panel and the slide list\nvar cand = []\nfor (const [k,v] of Object.entries(snapB.refs)) {\n if (/Slide 1[0-9]|Slide 2[0-4]|Slide Panel|Slides/i.test(v.name||\"\")) cand.push([k, v.role, v.name])\n}\nconsole.log(JSON.stringify(cand.slice(0,25), null, 1))\nconsole.log(\"lineCount\", snapB.snapshot.lineCount)\nconsole.log(snapB.snapshot.grep(\"Slide 19|Slide 24|Slide 18\", { context: 1, max: 12 }))", + "title": "Locating the new slides in the Impress panel" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@080649492454.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_017RmDurHWfVPuqFQ6u4ACYS", + "name": "execute", + "input": { + "code": "await app2.press({ key: 'escape' })\nawait app2.press({ key: 'escape' })\nawait wait({ waitTime: 1 })\n// scroll slide panel down to reach slides 18-24\nfor (let i = 0; i < 12; i++) {\n await machine.exec({ command: \"true\" })\n}\nawait app2.click({ ref: '273' }) // a slide thumbnail in the panel\nawait wait({ waitTime: 1 })\nawait app2.press({ key: 'end' }) // jump to last slide\nawait wait({ waitTime: 2 })\nawait app2.screenshot()\nconsole.log(\"ok\")", + "title": "Navigating to the last slides in Impress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@080714215622.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_01GVKEjpS8sz1SPK83eKyH3c", + "name": "execute", + "input": { + "code": "var lastChk = await machine.exec({ command: \"ls -la /home/user/Desktop/; md5sum /tmp/out.pptx /home/user/Desktop/stanford-nlp-seminar-talk-v2.pptx; cd /tmp && python3 verify.py 2>&1 | tail -14\", timeout: 180 })\nconsole.log(lastChk.stdout)", + "title": "Final integrity check of the saved presentation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@080736977499.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_48_20260923@080759999072.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/013.json b/demo/data/official/sai_traj/013.json new file mode 100644 index 0000000..2cc24a4 --- /dev/null +++ b/demo/data/official/sai_traj/013.json @@ -0,0 +1,1248 @@ +{ + "task": "013", + "run": "v21-ref5-rest-20260923T103950Z", + "score": 1.0, + "mean": 1.0, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref5-rest-20260923T103950Z/013/traj.jsonl", + "fetched_at": "2026-10-26T06:55:15Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01FUzWTFcdWeqNMJ3mfCwBTi", + "name": "execute", + "input": { + "code": "var machine = getMachine((await listMachines())[0].id)\nvar windows = await machine.listAppWindows()\nconsole.log(JSON.stringify(windows, null, 2))\nvar files = await machine.exec({ command: \"ls -la ~/Desktop\" })\nconsole.log(files.stdout)\n", + "title": "Listing open windows and Desktop files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@104148780314.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01Ct848sjoAW56nLY28VMQwr", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"which pdftotext pdftoppm python3; pdftotext ~/Desktop/prize-items.pdf /tmp/prize.txt && wc -l /tmp/prize.txt && cat /tmp/prize.txt\" })\nconsole.log(r.stdout)\nconsole.log(\"ERR:\", r.stderr)\n", + "title": "Extracting text from the prize items PDF" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@104200328474.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_014H5u3B1RR5CNRa17CoDsWL", + "name": "execute", + "input": { + "code": "var r2 = await machine.exec({ command: \"cd /tmp && pdftoppm -r 100 -png ~/Desktop/prize-items.pdf page && ls -la /tmp/page*\" })\nconsole.log(r2.stdout, r2.stderr)\n", + "title": "Rendering PDF pages as images" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@104212643833.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01MBpVgfoJQK7BUf3jT3Euqf", + "name": "execute", + "input": { + "code": "var pdfText = await machine.readFile({ path: \"/tmp/prize.txt\" })\nconsole.log(pdfText)\n", + "title": "Reading the full extracted PDF text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@104227014865.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_01CXUmyKNz1z1phmfNpcVVSV", + "name": "execute", + "input": { + "code": "var p = await machine.writeToFile({ text: \"test\", path: \"probe.txt\", overwrite: true })\nconsole.log(\"cache path:\", p)\nvar r3 = await machine.exec({ command: \"ls -d ~/SimularFiles 2>/dev/null; echo '---'; find / -maxdepth 4 -name 'SimularFiles' -type d 2>/dev/null\" })\nconsole.log(r3.stdout)\n", + "title": "Locating the SimularFiles folder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@104239243405.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_01V5WNzGbYXDZoChBVWQo5uR", + "name": "execute", + "input": { + "code": "console.log(\"LEN\", pdfText.length)\nconsole.log(pdfText.slice(0, 700))\n", + "title": "Reading the requirements section text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@104253741902.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01HsMYxD7y2LRSHpWN41pzQL", + "name": "execute", + "input": { + "code": "console.log(pdfText.slice(700))\n", + "title": "Reading remaining PDF text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@104304879953.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01U4ftwgvgSgudezvgSELusP", + "name": "execute", + "input": { + "code": "console.log(pdfText.slice(150, 620))\n", + "title": "Reading the middle requirements text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@104317212509.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_017J7wDfaZeZq1DaPtdP5qWB", + "name": "execute", + "input": { + "code": "console.log(JSON.stringify(windows, null, 2))\n", + "title": "Checking the list of open windows" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@104333140379.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_01LpSTqsvXdVSUM8uSvQiuZV", + "name": "execute", + "input": { + "code": "var chrome = await machine.getAppWindow('FormCraft - Google Chrome')\nawait chrome.activate()\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Opening the FormCraft window in Chrome" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@104349317970.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01XtNY9T7qh1wen3c3uXYXN3", + "name": "execute", + "input": { + "code": "var snap = await chrome.snapshot()\nconsole.log(snap.snapshot.outline())\n", + "title": "Reading the form structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@104407717859.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_019YjA5aWssMsFPSgLAp4tNp", + "name": "execute", + "input": { + "code": "await chrome.click({ ref: '163' })\nawait wait({ waitTime: 1 })\nvar pdfWin = await machine.getAppWindow('prize-items.pdf')\nawait pdfWin.activate()\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Dismissing dialog and opening the PDF viewer" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@104432960985.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01JbtHifAowNZ3xDv2Dd9kag", + "name": "execute", + "input": { + "code": "// Maximize the document viewer window\nawait pdfWin.press({ key: 'super+up' })\nawait wait({ waitTime: 1 })\nawait pdfWin.press({ key: 'ctrl+l' })\nawait wait({ waitTime: 1 })\nawait machine.type({ text: '2', withReturn: true })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Maximizing viewer and going to page 2" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@104455864838.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01CKU1YGLwFA9XQ5EL8Uffbg", + "name": "execute", + "input": { + "code": "await machine.click({ x: 657, y: 100 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Maximizing the PDF viewer window" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@104518294541.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01MqQNqYfe8SaJ7VQTgaMWSg", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"true\" })\n// scroll down within the document\nfor (var i=0;i<5;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling to see the second row of items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@104541437651.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01AMs5NhJAtYyGYgVXC4DLvw", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 3 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@104557804194.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01Mkomi24t7PT8szXrdfnPib", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Verifying page 2 item images and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@104613859901.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01BFwPm25K6bYp1eHASnjH6Z", + "name": "execute", + "input": { + "code": "for (var i=0;i<6;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Confirming the second row prices on page 2" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@104629932700.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_01B9KAbZ18kwhM2GvGeZbaLt", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1000, y: 700 })\nfor (var i=0;i<4;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Reviewing page 2 items in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@104648265811.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_01HsQoFZbVNVgbXimFE7G8hD", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Confirming page 3 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@104704294551.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01TWMYpGZUgp3HN1oDc7t7FD", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Verifying page 2 catalogue items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@104719860312.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_01MZ2asaQouwmLGUcV4AtsSw", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1060, y: 700 })\nfor (var i=0;i<6;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling down to read row 2 prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@104739582680.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_014VSqA3aBiWJmNKQH3VBurG", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 3 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@104755112679.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01FzDftWBrwGnSoy8Vcmw4qd", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 2 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@104810467112.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_01Hc1qKo4u6Qs3KGeWJx7yBg", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1060, y: 600 })\nfor (var i=0;i<7;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling to see row 2 prices on page 2" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@104829662834.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_019wj93iQ7Z5KMYQq2nDYAgH", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 3 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@104844542376.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01RJBS15u4aha6YnnQGYeg67", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 2 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@104859348527.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01MP5fgoL3W8jq5CRxFso62g", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1060, y: 600 })\nfor (var i=0;i<8;i++){ await machine.press({ key: 'down' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling to verify second row prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@104918779078.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01KjQwyU2EHadBahtjjRSqbP", + "name": "execute", + "input": { + "code": "for (var i=0;i<8;i++){ await machine.press({ key: 'up' }) }\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling up to view row 1 items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@104934866036.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01XWjLskCPtKNvEyYgLFWwE1", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 3 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@104953372999.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01VQjRt42b8A3daZAEYLukgZ", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 2 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@105008471669.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01Fo3DFVV6V98bMX7j1Q55pu", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1060, y: 600 })\nawait machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling to see row 2 prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@105025361635.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_01J9652vgwHp84vzuG2e5aQu", + "name": "execute", + "input": { + "code": "await machine.press({ key: 'pageUp' })\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Scrolling up to verify row 1 of page 2" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@105041158982.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_017m39iLqm6srhDYias4AVvn", + "name": "execute", + "input": { + "code": "var prices = {\n \"Balloons\": 40, \"Candy Bar\": 24, \"Chocolate Box\": 32, \"Coasters\": 20, \"Decorative Lights\": 47,\n \"Fine Wine\": 60, \"Gourmet Cheese\": 65, \"Keychain\": 25, \"Noise Maker\": 31, \"Notepad\": 24,\n \"Party Hat\": 31, \"Pen\": 24, \"Premium Champagne\": 126, \"Sparkling Cider\": 30, \"Sticker\": 25\n}\nconsole.log(\"total all:\", Object.values(prices).reduce((a,b)=>a+b,0))\nawait machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Recording price map and verifying page 3" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@105102889362.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_01D8K1VTEj31vdX9DQSoYRxA", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1 })\nawait pdfWin.screenshot()\n", + "title": "Verifying page 2 items and prices" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@105120347760.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_01QhXnwJ79G6itQLUpEyAMyw", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 2 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@105136140940.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_01SAxuTisNuCoYgo3XAgHZsa", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 523 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 3 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@105153391196.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01LNv3wyUKYs4uiA4VWS8T8f", + "name": "execute", + "input": { + "code": "await machine.click({ x: 133, y: 340 })\nawait wait({ waitTime: 2 })\nawait pdfWin.screenshot()\n", + "title": "Viewing page 2 of the catalogue" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@105209320163.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_01FWemmPFr8QnENzXzTGon1X", + "name": "execute", + "input": { + "code": "await chrome.activate()\nawait wait({ waitTime: 2 })\nvar s = await chrome.snapshot()\nconsole.log(\"lines:\", s.snapshot.lineCount)\nconsole.log(s.snapshot.read(1, 180))\n", + "title": "Reading the FormCraft form structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@105232436611.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_01TH9r7QJrCh1paJJQ3vjzTB", + "name": "execute", + "input": { + "code": "console.log(s.snapshot.read(180, 279))\n", + "title": "Reading the form fields in the page" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@105247947575.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_01DER85V3i5cvfDY9RLbNsBT", + "name": "execute", + "input": { + "code": "var py = `\nimport itertools, json\n\nprices = {\n \"Balloons\":40,\"Candy Bar\":24,\"Chocolate Box\":32,\"Coasters\":20,\"Decorative Lights\":47,\n \"Fine Wine\":60,\"Gourmet Cheese\":65,\"Keychain\":25,\"Noise Maker\":31,\"Notepad\":24,\n \"Party Hat\":31,\"Pen\":24,\"Premium Champagne\":126,\"Sparkling Cider\":30,\"Sticker\":25}\nnames = list(prices.keys())\nn = len(names)\n\n# valid size tuples: n1:n2 in [1:3,1:2] -> 2*n1<=n2<=3*n1 ; same n2:n3 ; n3:n4 in [1:2,1:1] -> n3<=n4<=2*n3\nsizes = []\nfor n1 in range(1, n):\n for n2 in range(2*n1, 3*n1+1):\n for n3 in range(2*n2, 3*n2+1):\n for n4 in range(n3, 2*n3+1):\n if n1+n2+n3+n4 <= n:\n sizes.append((n1,n2,n3,n4))\nprint(\"size options:\", sizes)\n\nbest = []\nidxs = list(range(n))\npv = [prices[k] for k in names]\n\nfor (n1,n2,n3,n4) in sizes:\n for S1 in itertools.combinations(idxs, n1):\n v1 = sum(pv[i] for i in S1)\n r1 = [i for i in idxs if i not in S1]\n for S2 in itertools.combinations(r1, n2):\n v2 = sum(pv[i] for i in S2)\n if abs(v1-v2) > 20: continue\n if v1+v2 > 500: continue\n r2 = [i for i in r1 if i not in S2]\n lo2, hi2 = min(v1,v2), max(v1,v2)\n for S3 in itertools.combinations(r2, n3):\n v3 = sum(pv[i] for i in S3)\n lo3, hi3 = min(lo2,v3), max(hi2,v3)\n if hi3-lo3 > 20: continue\n if v1+v2+v3 > 500: continue\n r3 = [i for i in r2 if i not in S3]\n for S4 in itertools.combinations(r3, n4):\n v4 = sum(pv[i] for i in S4)\n lo, hi = min(lo3,v4), max(hi3,v4)\n if hi-lo > 20: continue\n tot = v1+v2+v3+v4\n if tot > 500: continue\n best.append((tot, -(hi-lo), (n1,n2,n3,n4), S1,S2,S3,S4, (v1,v2,v3,v4)))\n\nbest.sort(key=lambda x: (x[0], x[1]), reverse=True)\nprint(\"solutions found:\", len(best))\nfor b in best[:10]:\n tot, negspread, sz, S1,S2,S3,S4, vs = b\n print(\"total=\",tot,\"spread=\",-negspread,\"sizes=\",sz,\"values=\",vs)\n print(\" 1st:\", [names[i] for i in S1])\n print(\" 2nd:\", [names[i] for i in S2])\n print(\" 3rd:\", [names[i] for i in S3])\n print(\" part:\", [names[i] for i in S4])\n`;\nawait machine.writeToFile({ text: py, path: \"/tmp/solve.py\", overwrite: true })\nvar out = await machine.exec({ command: \"cd /tmp && timeout 600 python3 solve.py\", timeout: 600 })\nconsole.log(out.stdout.slice(0, 4000))\nconsole.log(\"ERR\", out.stderr.slice(0,1000))\n", + "title": "Solving the prize allocation optimization" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@105352911953.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_018TNJe9aPv4dRp57VDbZLqR", + "name": "execute", + "input": { + "code": "console.log(s.snapshot.grep(\"textbox|Total|Submit|button \\\"\", { max: 60 }))\n", + "title": "Locating total amount fields and submit button" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@105440512022.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_01UC87ncFh81rspr9o3RRNpF", + "name": "execute", + "input": { + "code": "var py2 = `\nimport itertools, json\nfrom collections import defaultdict\nprices = {\n \"Balloons\":40,\"Candy Bar\":24,\"Chocolate Box\":32,\"Coasters\":20,\"Decorative Lights\":47,\n \"Fine Wine\":60,\"Gourmet Cheese\":65,\"Keychain\":25,\"Noise Maker\":31,\"Notepad\":24,\n \"Party Hat\":31,\"Pen\":24,\"Premium Champagne\":126,\"Sparkling Cider\":30,\"Sticker\":25}\nnames=list(prices.keys()); n=len(names); pv=[prices[k] for k in names]; idxs=list(range(n))\nsizes=[]\nfor n1 in range(1,n):\n for n2 in range(2*n1,3*n1+1):\n for n3 in range(2*n2,3*n2+1):\n for n4 in range(n3,2*n3+1):\n if n1+n2+n3+n4<=n: sizes.append((n1,n2,n3,n4))\ngroups=defaultdict(list)\nfor (n1,n2,n3,n4) in sizes:\n for S1 in itertools.combinations(idxs,n1):\n v1=sum(pv[i] for i in S1); r1=[i for i in idxs if i not in S1]\n for S2 in itertools.combinations(r1,n2):\n v2=sum(pv[i] for i in S2)\n if abs(v1-v2)>20 or v1+v2>500: continue\n r2=[i for i in r1 if i not in S2]; lo2,hi2=min(v1,v2),max(v1,v2)\n for S3 in itertools.combinations(r2,n3):\n v3=sum(pv[i] for i in S3); lo3,hi3=min(lo2,v3),max(hi2,v3)\n if hi3-lo3>20 or v1+v2+v3>500: continue\n r3=[i for i in r2 if i not in S3]\n for S4 in itertools.combinations(r3,n4):\n v4=sum(pv[i] for i in S4); lo,hi=min(lo3,v4),max(hi3,v4)\n if hi-lo>20: continue\n tot=v1+v2+v3+v4\n if tot!=500: continue\n groups[((n1,n2,n3,n4),hi-lo)].append((S1,S2,S3,S4,(v1,v2,v3,v4)))\nfor k in sorted(groups, key=lambda k:(k[1],k[0])):\n print(k, \"count=\", len(groups[k]))\n if k[1]<=2:\n for sol in groups[k][:3]:\n S1,S2,S3,S4,vs=sol\n print(\" v\",vs,\"|1st\",[names[i] for i in S1],\"|2nd\",[names[i] for i in S2],\"|3rd\",[names[i] for i in S3],\"|part\",[names[i] for i in S4])\n`;\nawait machine.writeToFile({ text: py2, path: \"/tmp/solve2.py\", overwrite: true })\nvar out2 = await machine.exec({ command: \"cd /tmp && timeout 900 python3 solve2.py\", timeout: 900 })\nconsole.log(out2.stdout.slice(0, 5000))\nconsole.log(\"ERR\", out2.stderr.slice(0,500))\n", + "title": "Checking all $500 solutions by tier sizes" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@105520454078.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_01LUksqPXpzKFBVEoa4gwFuF", + "name": "execute", + "input": { + "code": "var solution = {\n \"First Prize\": { items: [\"Premium Champagne\"], total: 126 },\n \"Second Prize\": { items: [\"Fine Wine\",\"Gourmet Cheese\"], total: 125 },\n \"Third Prize\": { items: [\"Balloons\",\"Candy Bar\",\"Noise Maker\",\"Sparkling Cider\"], total: 125 },\n \"Participation Prize\": { items: [\"Chocolate Box\",\"Coasters\",\"Decorative Lights\",\"Keychain\"], total: 124 }\n}\nconsole.log(s.snapshot.grep(\"checkbox\", { max: 70 }))\n", + "title": "Finding checkbox elements in the form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@105538467220.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_01RhK3Xt1cmyA3kzF5X1twGR", + "name": "execute", + "input": { + "code": "await chrome.press({ key: 'ctrl+Home' })\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Scrolling to the top of the form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@105604869692.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_01F7SU63kjYhNQCG8gVbdDRW", + "name": "execute", + "input": { + "code": "await machine.click({ x: 420, y: 600 })\nawait machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1 })\nvar s2 = await chrome.snapshot()\nconsole.log(s2.snapshot.grep(\"checkbox|textbox|Total Amount|Prize:\", { max: 40 }))\n", + "title": "Scrolling down to find Premium Champagne" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@105630245532.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_01PLZ4Lv7o13HPYCuMEdQaJC", + "name": "execute", + "input": { + "code": "await chrome.click({ ref: '119' }) // First Prize: Premium Champagne\nawait wait({ waitTime: 0.5 })\nawait chrome.type({ ref: '134', text: '126' }) // First Prize Total Amount\nawait wait({ waitTime: 0.5 })\nawait chrome.click({ ref: '161' }) // Second Prize: Fine Wine\nawait wait({ waitTime: 0.5 })\nawait chrome.click({ ref: '165' }) // Second Prize: Gourmet Cheese\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Selecting first and second prize items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@105652923785.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": { + "id": "toolu_bdrk_01WkXLnLm4yf9btaUAgRpcKf", + "name": "execute", + "input": { + "code": "await machine.click({ x: 420, y: 600 })\nawait machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1 })\nvar s3 = await chrome.snapshot()\nconsole.log(s3.snapshot.grep(\"checkbox|textbox|panel \\\"\", { max: 45 }))\n", + "title": "Scrolling to the second prize total field" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_48_20260923@105712844482.png", + "osworld_action": "WAIT" + }, + { + "step_num": 49, + "action": { + "id": "toolu_bdrk_01A1Pk9bGD2FKX8L2DoZvb7y", + "name": "execute", + "input": { + "code": "console.log(s3.snapshot.read(254, 270))\nconsole.log(\"-----\")\nconsole.log(s3.snapshot.read(325, 340))\n", + "title": "Checking form section ordering in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_49_20260923@105757304607.png", + "osworld_action": "WAIT" + }, + { + "step_num": 50, + "action": { + "id": "toolu_bdrk_013oipAtv9qmbnwkqYBdtfDi", + "name": "execute", + "input": { + "code": "console.log(s3.snapshot.grep(\"checked\", { max: 30 }))\n", + "title": "Listing all currently checked boxes" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_50_20260923@105816452949.png", + "osworld_action": "WAIT" + }, + { + "step_num": 51, + "action": { + "id": "toolu_bdrk_01A6Vqt73A1EFTXTgoUxX89Y", + "name": "execute", + "input": { + "code": "console.log(JSON.stringify(s3.refs['154']))\nconsole.log(JSON.stringify(s3.refs['161']), JSON.stringify(s3.refs['165']))\n", + "title": "Confirming the second prize total field reference" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_51_20260923@105833889977.png", + "osworld_action": "WAIT" + }, + { + "step_num": 52, + "action": { + "id": "toolu_bdrk_01KKmARyF3tXsN8hnrX7jcQw", + "name": "execute", + "input": { + "code": "await chrome.type({ ref: '154', text: '125' }) // Second Prize Total\nawait wait({ waitTime: 0.5 })\nawait chrome.click({ ref: '161' }) // Third Prize: Balloons\nawait wait({ waitTime: 0.5 })\nawait chrome.click({ ref: '165' }) // Third Prize: Candy Bar\nawait wait({ waitTime: 1 })\nvar s4 = await chrome.snapshot()\nconsole.log(s4.snapshot.grep(\"checked\", { max: 30 }))\n", + "title": "Filling second prize total and third prize items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_52_20260923@105852192512.png", + "osworld_action": "WAIT" + }, + { + "step_num": 53, + "action": { + "id": "toolu_bdrk_011LroV4pV5jH1HGiGEhoUBf", + "name": "execute", + "input": { + "code": "await wait({ waitTime: 2 })\nvar s5 = await chrome.snapshot()\nconsole.log(s5.snapshot.grep(\"checked\", { max: 30 }))\nconsole.log(\"lineCount\", s5.snapshot.lineCount)\n", + "title": "Re-checking the checked state of all boxes" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_53_20260923@105919630546.png", + "osworld_action": "WAIT" + }, + { + "step_num": 54, + "action": { + "id": "toolu_bdrk_01Ts86HpkQGTpwJt2u2N2TWS", + "name": "execute", + "input": { + "code": "console.log(s5.snapshot.grep(\"Prize: \\\\*|Total Amount \\\\*\", { max: 20 }))\n", + "title": "Mapping section headers and total fields by line" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_54_20260923@105939451540.png", + "osworld_action": "WAIT" + }, + { + "step_num": 55, + "action": { + "id": "toolu_bdrk_01X1xnzu42ZPNKa3fdtEUhTb", + "name": "execute", + "input": { + "code": "await machine.click({ x: 420, y: 400 })\nawait machine.press({ key: 'ctrl+Home' })\nawait wait({ waitTime: 1.5 })\nawait chrome.screenshot()\n", + "title": "Scrolling to top to verify first prize section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_55_20260923@110006769909.png", + "osworld_action": "WAIT" + }, + { + "step_num": 56, + "action": { + "id": "toolu_bdrk_018QXCRSxSL5P27DWY4qr8qx", + "name": "execute", + "input": { + "code": "for (var k=0;k<4;k++){ await machine.press({ key: 'pageUp' }); await wait({ waitTime: 0.4 }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Scrolling up to inspect the first prize section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_56_20260923@110043502745.png", + "osworld_action": "WAIT" + }, + { + "step_num": 57, + "action": { + "id": "toolu_bdrk_013zmfJM3FNnkaZEpq3FJQYJ", + "name": "execute", + "input": { + "code": "await machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1.2 })\nawait chrome.screenshot()\n", + "title": "Checking the first prize selection" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_57_20260923@110103232992.png", + "osworld_action": "WAIT" + }, + { + "step_num": 58, + "action": { + "id": "toolu_bdrk_01XmCNbdxJycuFgVfWXZUL3S", + "name": "execute", + "input": { + "code": "for (var k2=0;k2<3;k2++){ await machine.press({ key: 'pageDown' }); await wait({ waitTime: 0.4 }) }\nawait wait({ waitTime: 1 })\nvar s6 = await chrome.snapshot()\n// figure out which refs are visible and their line numbers\nvar vis = s6.snapshot.grep(\"checkbox \\\"|textbox \\\"\", { max: 60 })\nconsole.log(vis)\n", + "title": "Scrolling down toward the Third Prize section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_58_20260923@110127518556.png", + "osworld_action": "WAIT" + }, + { + "step_num": 59, + "action": { + "id": "toolu_bdrk_01S978vc4ZT5cjEtbJjiQAbQ", + "name": "execute", + "input": { + "code": "for (var z=0;z<5;z++){ await chrome.press({ key: 'ctrl+minus' }); await wait({ waitTime: 0.4 }) }\nawait wait({ waitTime: 1 })\nawait machine.press({ key: 'ctrl+Home' })\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Zooming out to view the entire form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_59_20260923@110209811401.png", + "osworld_action": "WAIT" + }, + { + "step_num": 60, + "action": { + "id": "toolu_bdrk_01Q6eivnVvmFvLoa1PddhveE", + "name": "execute", + "input": { + "code": "console.log(\"s6 checked:\"); console.log(s6.snapshot.grep(\"\\\\[checked\\\\]\", { max: 40 }))\nvar s7 = await chrome.snapshot()\nconsole.log(\"s7 checked:\"); console.log(s7.snapshot.grep(\"\\\\[checked\\\\]\", { max: 40 }))\n", + "title": "Comparing checked state across snapshots" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_60_20260923@110232806037.png", + "osworld_action": "WAIT" + }, + { + "step_num": 61, + "action": { + "id": "toolu_bdrk_01JgsGAF5oC94ggERBmqWrsd", + "name": "execute", + "input": { + "code": "console.log(\"ref132:\", JSON.stringify(s7.refs['132']))\nconsole.log(s7.snapshot.read(403, 425))\n", + "title": "Investigating the reported checked state" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_61_20260923@110259575734.png", + "osworld_action": "WAIT" + }, + { + "step_num": 62, + "action": { + "id": "toolu_bdrk_01FD1v94aM3Pg7dhqv6VMZHG", + "name": "execute", + "input": { + "code": "var probe = await machine.exec({ command: \"curl -s --max-time 5 http://127.0.0.1:9222/json/version || echo NOCDP\" })\nconsole.log(probe.stdout.slice(0,500))\n", + "title": "Checking for a read-only debug endpoint" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_62_20260923@110332332707.png", + "osworld_action": "WAIT" + }, + { + "step_num": 63, + "action": { + "id": "toolu_bdrk_015SWygc7Gv2ZqJ9FZXs7Rvf", + "name": "execute", + "input": { + "code": "var chk = await machine.exec({ command: \"python3 -c \\\"import websocket; print('websocket-client ok')\\\" 2>&1; python3 -c \\\"import websockets; print('websockets ok')\\\" 2>&1; curl -s --max-time 5 http://127.0.0.1:9222/json/list | head -c 1500\" })\nconsole.log(chk.stdout)\n", + "title": "Checking available tooling and page targets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_63_20260923@110346450548.png", + "osworld_action": "WAIT" + }, + { + "step_num": 64, + "action": { + "id": "toolu_bdrk_01R4aAmMHT7GWcTGuLpwLXpe", + "name": "execute", + "input": { + "code": "var ws = `\nimport socket, base64, os, json, struct, urllib.request, sys\n\ndef get_ws_url():\n d = json.load(urllib.request.urlopen(\"http://127.0.0.1:9222/json/list\"))\n for t in d:\n if t.get(\"type\")==\"page\" and \"formcraft\" in t.get(\"url\",\"\"):\n return t[\"webSocketDebuggerUrl\"]\n raise SystemExit(\"no target\")\n\nclass WS:\n def __init__(self, url):\n assert url.startswith(\"ws://\")\n rest = url[5:]\n hostport, path = rest.split(\"/\",1)\n path = \"/\"+path\n host, port = hostport.split(\":\")\n self.s = socket.create_connection((host,int(port)), timeout=15)\n key = base64.b64encode(os.urandom(16)).decode()\n req = (\"GET %s HTTP/1.1\\\\r\\\\nHost: %s\\\\r\\\\nUpgrade: websocket\\\\r\\\\nConnection: Upgrade\\\\r\\\\n\"\n \"Sec-WebSocket-Key: %s\\\\r\\\\nSec-WebSocket-Version: 13\\\\r\\\\n\\\\r\\\\n\") % (path, hostport, key)\n self.s.sendall(req.encode())\n buf=b\"\"\n while b\"\\\\r\\\\n\\\\r\\\\n\" not in buf:\n buf += self.s.recv(4096)\n self.buf = buf.split(b\"\\\\r\\\\n\\\\r\\\\n\",1)[1]\n def _read(self, n):\n while len(self.buf) < n:\n d = self.s.recv(65536)\n if not d: raise EOFError\n self.buf += d\n out, self.buf = self.buf[:n], self.buf[n:]\n return out\n def send(self, obj):\n payload = json.dumps(obj).encode()\n hdr = bytearray([0x81])\n n = len(payload)\n if n < 126: hdr.append(0x80 | n)\n elif n < 65536: hdr.append(0x80|126); hdr += struct.pack(\">H\", n)\n else: hdr.append(0x80|127); hdr += struct.pack(\">Q\", n)\n mask = os.urandom(4); hdr += mask\n masked = bytes(b ^ mask[i%4] for i,b in enumerate(payload))\n self.s.sendall(bytes(hdr)+masked)\n def recv(self):\n while True:\n b0, b1 = self._read(2)\n op = b0 & 0x0f\n ln = b1 & 0x7f\n if ln == 126: ln = struct.unpack(\">H\", self._read(2))[0]\n elif ln == 127: ln = struct.unpack(\">Q\", self._read(8))[0]\n data = self._read(ln)\n if op == 1: return json.loads(data.decode())\n if op == 8: raise EOFError\n\ndef evaluate(expr):\n w = WS(get_ws_url())\n w.send({\"id\":1,\"method\":\"Runtime.evaluate\",\"params\":{\"expression\":expr,\"returnByValue\":True,\"awaitPromise\":True}})\n while True:\n m = w.recv()\n if m.get(\"id\")==1:\n return m\n\nif __name__ == \"__main__\":\n expr = sys.stdin.read()\n r = evaluate(expr)\n res = r.get(\"result\",{}).get(\"result\",{})\n print(json.dumps(res.get(\"value\", res), indent=1)[:6000])\n`;\nawait machine.writeToFile({ text: ws, path: \"/tmp/cdp.py\", overwrite: true })\nvar readExpr = `(() => {\n const groups = [...document.querySelectorAll('input[type=checkbox]')];\n const out = groups.map(cb => {\n let lab = cb.closest('label');\n let name = lab ? lab.innerText.trim() : (cb.name||cb.value||'?');\n return { name, checked: cb.checked };\n });\n const texts = [...document.querySelectorAll('input[type=text], input[type=number], input:not([type])')].map(i=>({ph:i.placeholder||i.name||i.id, v:i.value}));\n return JSON.stringify({ n: out.length, out, texts });\n})()`;\nvar res = await machine.exec({ command: \"cd /tmp && printf '%s' \" + JSON.stringify(readExpr).replace(/'/g,\"'\\\\''\") + \" | python3 cdp.py\", timeout: 60 })\nconsole.log(res.stdout.slice(0,6000))\nconsole.log(\"ERR\", res.stderr.slice(0,1500))\n", + "title": "Reading the actual form state from the page" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_64_20260923@110411603796.png", + "osworld_action": "WAIT" + }, + { + "step_num": 65, + "action": { + "id": "toolu_bdrk_012aEHWw2ZsZSdRdNx3xWRBs", + "name": "execute", + "input": { + "code": "await machine.writeToFile({ text: readExpr, path: \"/tmp/expr.js\", overwrite: true })\nvar res2 = await machine.exec({ command: \"cd /tmp && python3 cdp.py < expr.js\", timeout: 60 })\nconsole.log(res2.stdout.slice(0,6000))\nconsole.log(\"ERR\", res2.stderr.slice(0,800))\n", + "title": "Reading the real checkbox states from the page" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_65_20260923@110423302708.png", + "osworld_action": "WAIT" + }, + { + "step_num": 66, + "action": { + "id": "toolu_bdrk_0128nNZnp47C2ure8NATWsZh", + "name": "execute", + "input": { + "code": "// --- helpers ---\nasync function verifyState() {\n var r = await machine.exec({ command: \"cd /tmp && python3 cdp.py < expr.js\", timeout: 60 })\n var raw = r.stdout.trim()\n // stdout is a JSON-quoted string containing JSON\n var inner = JSON.parse(raw)\n var data = JSON.parse(inner)\n var groupNames = [\"First\",\"Second\",\"Third\",\"Participation\"]\n var result = {}\n for (var g=0; g<4; g++) {\n var slice = data.out.slice(g*15, g*15+15)\n result[groupNames[g]] = slice.filter(x=>x.checked).map(x=>x.name)\n }\n result.totals = data.texts.map(t=>t.v)\n return result\n}\nasync function checkboxMap() {\n var sn = await chrome.snapshot()\n var txt = sn.snapshot.grep(\"checkbox \\\"\", { max: 100 })\n var map = {} // key \"section|name\" -> ref\n txt.split(\"\\n\").forEach(function(line){\n var m = line.match(/^\\s*(\\d+):.*checkbox \"([^\"]+)\"(?:\\s*\\[ref=(\\d+)\\])?/)\n if (!m) return\n var ln = parseInt(m[1]), name = m[2], ref = m[3]\n var sec = ln < 262 ? \"First\" : (ln < 332 ? \"Second\" : (ln < 402 ? \"Third\" : \"Participation\"))\n map[sec+\"|\"+name] = { line: ln, ref: ref || null }\n })\n return { snap: sn, map: map }\n}\nvar st = await verifyState()\nconsole.log(JSON.stringify(st))\n", + "title": "Setting up state verification helpers" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_66_20260923@110500664272.png", + "osworld_action": "WAIT" + }, + { + "step_num": 67, + "action": { + "id": "toolu_bdrk_01YEBNwY42znQq9NDfWPh678", + "name": "execute", + "input": { + "code": "var cm = await checkboxMap()\nvar keys = Object.keys(cm.map).filter(k=>cm.map[k].ref)\nconsole.log(\"visible with refs:\", JSON.stringify(keys))\n", + "title": "Finding which checkboxes are currently clickable" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_67_20260923@110525284327.png", + "osworld_action": "WAIT" + }, + { + "step_num": 68, + "action": { + "id": "toolu_bdrk_014zUDST4zAvZzn6fSogzhhC", + "name": "execute", + "input": { + "code": "await machine.move({ x: 420, y: 300 })\nawait wait({ waitTime: 1 })\nvar stA = await verifyState()\nconsole.log(\"CDP:\", JSON.stringify(stA))\nawait chrome.screenshot()\n", + "title": "Testing whether hover changes checkbox appearance" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_68_20260923@110558268471.png", + "osworld_action": "WAIT" + }, + { + "step_num": 69, + "action": { + "id": "toolu_bdrk_017EtbwNu1eFn2GXvu1ptCPB", + "name": "execute", + "input": { + "code": "var cm2 = await checkboxMap()\nvar targets = [\"Participation|Chocolate Box\",\"Participation|Decorative Lights\",\"Participation|Keychain\"]\nfor (var t of targets) {\n var e = cm2.map[t]\n console.log(t, \"->\", JSON.stringify(e))\n if (e && e.ref) { await chrome.click({ ref: e.ref }); await wait({ waitTime: 0.6 }) }\n}\nawait machine.move({ x: 420, y: 300 })\nawait wait({ waitTime: 1 })\nconsole.log(\"CDP:\", JSON.stringify(await verifyState()))\n", + "title": "Checking Participation Prize items" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_69_20260923@110638330536.png", + "osworld_action": "WAIT" + }, + { + "step_num": 70, + "action": { + "id": "toolu_bdrk_01Vs7jmKRGLnVhSfoLAp4Xja", + "name": "execute", + "input": { + "code": "var readExpr2 = `(() => {\n const cbs = [...document.querySelectorAll('input[type=checkbox]')];\n function groupOf(cb){\n let n = cb;\n while (n && n !== document.body) {\n n = n.parentElement;\n if (!n) break;\n const t = (n.innerText||'');\n const m = t.match(/(First Prize|Second Prize|Third Prize|Participation Prize):/);\n if (m) return m[1];\n }\n return '?';\n }\n const res = {};\n for (const cb of cbs) {\n const g = groupOf(cb);\n const lab = cb.closest('label');\n const name = (lab? lab.innerText.trim() : (cb.value||cb.name||'?'));\n (res[g] = res[g] || []).push({name, checked: cb.checked});\n }\n const sel = {};\n for (const k in res) sel[k] = res[k].filter(x=>x.checked).map(x=>x.name);\n const counts = {}; for (const k in res) counts[k] = res[k].length;\n const texts = {};\n for (const i of document.querySelectorAll('input:not([type=checkbox]):not([type=radio]), textarea')) {\n texts[i.placeholder||i.name||i.id||'?'] = i.value;\n }\n return JSON.stringify({counts, sel, texts});\n})()`;\nawait machine.writeToFile({ text: readExpr2, path: \"/tmp/expr.js\", overwrite: true })\nasync function verify2() {\n var r = await machine.exec({ command: \"cd /tmp && python3 cdp.py < expr.js\", timeout: 60 })\n return JSON.parse(JSON.parse(r.stdout.trim()))\n}\nconsole.log(JSON.stringify(await verify2(), null, 1))\n", + "title": "Reading form state grouped by prize tier" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_70_20260923@110718483438.png", + "osworld_action": "WAIT" + }, + { + "step_num": 71, + "action": { + "id": "toolu_bdrk_01Mt5e7BjWgcnPZJAfoeLcKU", + "name": "execute", + "input": { + "code": "await machine.click({ x: 420, y: 300 })\nawait machine.press({ key: 'pageUp' })\nawait wait({ waitTime: 1 })\nvar cm3 = await checkboxMap()\nconsole.log(\"visible:\", JSON.stringify(Object.keys(cm3.map).filter(k=>cm3.map[k].ref)))\n", + "title": "Scrolling up to reach the Third Prize section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_71_20260923@110751321123.png", + "osworld_action": "WAIT" + }, + { + "step_num": 72, + "action": { + "id": "toolu_bdrk_01TM6Ufq57BJAoKGo6efeZKT", + "name": "execute", + "input": { + "code": "console.log(\"NoiseMaker:\", JSON.stringify(cm3.map[\"Third|Noise Maker\"]), \"SparklingCider:\", JSON.stringify(cm3.map[\"Third|Sparkling Cider\"]))\nawait chrome.click({ ref: cm3.map[\"Third|Noise Maker\"].ref })\nawait wait({ waitTime: 0.6 })\nawait chrome.click({ ref: cm3.map[\"Third|Sparkling Cider\"].ref })\nawait wait({ waitTime: 0.6 })\n// park pointer on a neutral (non-row) spot: page background left of the card\nawait machine.click({ x: 300, y: 300 })\nawait wait({ waitTime: 1 })\nconsole.log(JSON.stringify(await verify2(), null, 1))\n", + "title": "Selecting Noise Maker and Sparkling Cider for Third Prize" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_72_20260923@110836390039.png", + "osworld_action": "WAIT" + }, + { + "step_num": 73, + "action": { + "id": "toolu_bdrk_014UNhNHrSxvhNy7CnX9kQo6", + "name": "execute", + "input": { + "code": "async function ensure(sec, name, want) {\n for (var attempt=0; attempt<4; attempt++) {\n var st = await verify2()\n var cur = (st.sel[sec+\" Prize\"]||[]).includes(name)\n if (cur === want) { console.log(\"OK\", sec, name, \"=\", want); return true }\n var m = await checkboxMap()\n var key = (sec===\"Participation\"?\"Participation\":sec)+\"|\"+name\n var e = m.map[key]\n if (!e || !e.ref) { console.log(\"NOT VISIBLE\", key); return false }\n await chrome.click({ ref: e.ref })\n await wait({ waitTime: 0.8 })\n await machine.click({ x: 300, y: 300 }) // park pointer off the rows\n await wait({ waitTime: 0.8 })\n }\n console.log(\"FAILED\", sec, name)\n return false\n}\nawait ensure(\"Third\", \"Noise Maker\", true)\nconsole.log(JSON.stringify((await verify2()).sel, null, 1))\n", + "title": "Ensuring Noise Maker is selected for Third Prize" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_73_20260923@110915296605.png", + "osworld_action": "WAIT" + }, + { + "step_num": 74, + "action": { + "id": "toolu_bdrk_01Ap9Jgzrm7HXVPV9SPFndwd", + "name": "execute", + "input": { + "code": "var diag = `(() => {\n const out=[];\n for (const cb of document.querySelectorAll('input[type=checkbox]')) {\n const lab = cb.closest('label');\n const nm = lab? lab.innerText.trim():'?';\n if (nm==='Noise Maker') out.push({nm, checked: cb.checked, aria: cb.getAttribute('aria-checked'), cls: cb.className, id: cb.id, name: cb.name, rect: cb.getBoundingClientRect().toJSON()});\n }\n return JSON.stringify(out);\n})()`;\nawait machine.writeToFile({ text: diag, path: \"/tmp/diag.js\", overwrite: true })\nvar d1 = await machine.exec({ command: \"cd /tmp && python3 cdp.py < diag.js\", timeout: 60 })\nconsole.log(\"BEFORE:\", d1.stdout.slice(0,1200))\n", + "title": "Inspecting the Noise Maker checkbox elements" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_74_20260923@110957824274.png", + "osworld_action": "WAIT" + }, + { + "step_num": 75, + "action": { + "id": "toolu_bdrk_01FM6T5cngCVkzjwph1KRrjD", + "name": "execute", + "input": { + "code": "var cm4 = await checkboxMap()\nvar nm = cm4.map[\"Third|Noise Maker\"]\nconsole.log(\"nm entry:\", JSON.stringify(nm))\nconsole.log(\"bounds:\", JSON.stringify(cm4.snap.refs[nm.ref]))\nvar b = cm4.snap.refs[nm.ref].bounds\nvar cx = Math.round((b.left+b.right)/2), cy = Math.round((b.top+b.bottom)/2)\nconsole.log(\"clicking at\", cx, cy)\nawait machine.click({ x: cx, y: cy })\nawait wait({ waitTime: 1 })\nvar d2 = await machine.exec({ command: \"cd /tmp && python3 cdp.py < diag.js\", timeout: 60 })\nconsole.log(\"AFTER:\", d2.stdout.replace(/\\\\\"/g,'\"').slice(0,900))\n", + "title": "Clicking Noise Maker by coordinates" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_75_20260923@111039703912.png", + "osworld_action": "WAIT" + }, + { + "step_num": 76, + "action": { + "id": "toolu_bdrk_01CFoQYqp3aSS5bJreZG3KZf", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1000, y: 574 })\nawait wait({ waitTime: 1 })\nvar d3 = await machine.exec({ command: \"cd /tmp && python3 cdp.py < diag.js\", timeout: 60 })\nconsole.log(\"AFTER label click:\", d3.stdout.replace(/\\\\\"/g,'\"').replace(/\\},\\{/g,\"}\\n{\").slice(0,1200))\n", + "title": "Clicking the Noise Maker row label" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_76_20260923@111110202172.png", + "osworld_action": "WAIT" + }, + { + "step_num": 77, + "action": { + "id": "toolu_bdrk_01URCB6m1eDp3emjVJiY9LEh", + "name": "execute", + "input": { + "code": "var allExpr = `(() => {\n const items=[];\n for (const cb of document.querySelectorAll('input[type=checkbox]')) {\n const lab = cb.closest('label');\n const r = cb.getBoundingClientRect();\n items.push({ g: cb.name.replace('field-','').replace('_prize',''), n: lab?lab.innerText.trim():'?', c: cb.checked, y: r.top, x: r.left });\n }\n const texts={};\n for (const i of document.querySelectorAll('input:not([type=checkbox])')) if(i.name) texts[i.name.replace('field-','')] = i.value;\n return JSON.stringify({ ih: window.innerHeight, sy: window.scrollY, items, texts });\n})()`;\nawait machine.writeToFile({ text: allExpr, path: \"/tmp/all.js\", overwrite: true })\nvar OFFX = 70, OFFY = 114\nasync function getAll() {\n var r = await machine.exec({ command: \"cd /tmp && python3 cdp.py < all.js\", timeout: 60 })\n return JSON.parse(JSON.parse(r.stdout.trim()))\n}\nasync function scrollPage(dir) {\n await machine.click({ x: 300, y: 500 })\n await machine.press({ key: dir > 0 ? 'pageDown' : 'pageUp' })\n await wait({ waitTime: 0.9 })\n}\nasync function setBox(group, name, want) {\n for (var a=0; a<10; a++) {\n var st = await getAll()\n var it = st.items.find(x => x.g===group && x.n===name)\n if (!it) { console.log(\"missing\", group, name); return false }\n if (it.c === want) { console.log(\" ok\", group, name, \"=\", want); return true }\n if (it.y < 30) { await scrollPage(-1); continue }\n if (it.y > st.ih - 50) { await scrollPage(1); continue }\n await machine.click({ x: 1000, y: Math.round(it.y + OFFY + 8) })\n await wait({ waitTime: 0.9 })\n }\n console.log(\" FAILED\", group, name); return false\n}\nvar st0 = await getAll()\nconsole.log(\"ih\", st0.ih, \"scrollY\", st0.sy, \"texts\", JSON.stringify(st0.texts))\nconsole.log(\"checked:\", JSON.stringify(st0.items.filter(i=>i.c).map(i=>i.g+\"|\"+i.n)))\n", + "title": "Setting up reliable checkbox control helpers" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_77_20260923@111148500457.png", + "osworld_action": "WAIT" + }, + { + "step_num": 78, + "action": { + "id": "toolu_bdrk_01ENjELgsj8cNro9xPro7fPN", + "name": "execute", + "input": { + "code": "var allExpr2 = allExpr.replace(\"if(i.name) texts[i.name.replace('field-','')] = i.value;\", \"{const k=(i.placeholder||i.name||i.id||'?').replace('field-',''); texts[k]=i.value;}\")\nawait machine.writeToFile({ text: allExpr2, path: \"/tmp/all.js\", overwrite: true })\nawait setBox(\"second\", \"Premium Champagne\", false)\nawait setBox(\"participation\", \"Coasters\", true)\nvar st1 = await getAll()\nconsole.log(\"checked:\", JSON.stringify(st1.items.filter(i=>i.c).map(i=>i.g+\"|\"+i.n), null, 1))\nconsole.log(\"texts:\", JSON.stringify(st1.texts))\n", + "title": "Unchecking stray item and adding Coasters" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_78_20260923@111213114278.png", + "osworld_action": "WAIT" + }, + { + "step_num": 79, + "action": { + "id": "toolu_bdrk_01HwPiGvgHX1jGW3nSX3q9Au", + "name": "execute", + "input": { + "code": "var allExpr3 = allExpr2.replace(\"texts[k]=i.value;\", \"texts[k]={v:i.value, y:i.getBoundingClientRect().top};\")\nawait machine.writeToFile({ text: allExpr3, path: \"/tmp/all.js\", overwrite: true })\nasync function setText(key, value) {\n for (var a=0; a<10; a++) {\n var st = await getAll()\n var f = st.texts[key]\n if (!f) { console.log(\"no field\", key, JSON.stringify(Object.keys(st.texts))); return false }\n if (String(f.v) === String(value)) { console.log(\" ok\", key, \"=\", value); return true }\n if (f.y < 30) { await scrollPage(-1); continue }\n if (f.y > st.ih - 60) { await scrollPage(1); continue }\n await machine.click({ x: 1000, y: Math.round(f.y + OFFY + 22) })\n await wait({ waitTime: 0.5 })\n await machine.press({ key: 'a', ctrl: true })\n await wait({ waitTime: 0.3 })\n await machine.type({ text: String(value) })\n await wait({ waitTime: 0.8 })\n }\n console.log(\" FAILED\", key); return false\n}\nawait setText(\"third_prize_amount\", \"125\")\nawait setText(\"participation_prize_amount\", \"124\")\nvar st2 = await getAll()\nconsole.log(\"FINAL checked:\", JSON.stringify(st2.items.filter(i=>i.c).map(i=>i.g+\"|\"+i.n)))\nconsole.log(\"FINAL texts:\", JSON.stringify(Object.fromEntries(Object.entries(st2.texts).map(([k,v])=>[k,v.v]))))\n", + "title": "Filling the remaining total amount fields" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_79_20260923@111244965229.png", + "osworld_action": "WAIT" + }, + { + "step_num": 80, + "action": { + "id": "toolu_bdrk_01Pb5HjZWiMeNuAVM7owFZ1H", + "name": "execute", + "input": { + "code": "var btnExpr = `(() => {\n const bs = [...document.querySelectorAll('button, input[type=submit], a')].map(b=>({tag:b.tagName, t:(b.innerText||b.value||'').trim().slice(0,60), y: b.getBoundingClientRect().top, type:b.type||''}));\n return JSON.stringify({ih:window.innerHeight, sh:document.body.scrollHeight, sy:window.scrollY, bs, title:document.title, url:location.href, bodyTail: document.body.innerText.slice(-600)});\n})()`;\nawait machine.writeToFile({ text: btnExpr, path: \"/tmp/btn.js\", overwrite: true })\nvar bout = await machine.exec({ command: \"cd /tmp && python3 cdp.py < btn.js\", timeout: 60 })\nvar bdata = JSON.parse(JSON.parse(bout.stdout.trim()))\nconsole.log(JSON.stringify(bdata.bs, null, 1))\nconsole.log(\"scrollH\", bdata.sh, \"scrollY\", bdata.sy, \"ih\", bdata.ih)\nconsole.log(\"TAIL:\", bdata.bodyTail)\n", + "title": "Looking for a submit button on the form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_80_20260923@111306971672.png", + "osworld_action": "WAIT" + }, + { + "step_num": 81, + "action": { + "id": "toolu_bdrk_01QLrLNPGBw5vhiAhiFtxZnu", + "name": "execute", + "input": { + "code": "await machine.move({ x: 300, y: 900 })\nawait wait({ waitTime: 0.8 })\nawait chrome.screenshot()\n", + "title": "Viewing the Participation Prize section and submit button" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_81_20260923@111331102872.png", + "osworld_action": "WAIT" + }, + { + "step_num": 82, + "action": { + "id": "toolu_bdrk_01Aagor4bMRUUKtwN3ivkz28", + "name": "execute", + "input": { + "code": "var pre = await getAll()\nconsole.log(\"PRE-SUBMIT checked:\", JSON.stringify(pre.items.filter(i=>i.c).map(i=>i.g+\"|\"+i.n)))\nconsole.log(\"PRE-SUBMIT texts:\", JSON.stringify(Object.fromEntries(Object.entries(pre.texts).map(([k,v])=>[k,v.v]))))\nawait machine.click({ x: 987, y: 941 })\nawait wait({ waitTime: 3 })\nawait chrome.screenshot()\n", + "title": "Submitting the completed form" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_82_20260923@111354280558.png", + "osworld_action": "WAIT" + }, + { + "step_num": 83, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_83_20260923@111418601190.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/023.json b/demo/data/official/sai_traj/023.json new file mode 100644 index 0000000..96150cd --- /dev/null +++ b/demo/data/official/sai_traj/023.json @@ -0,0 +1,888 @@ +{ + "task": "023", + "run": "v21-r12-20260923T074850Z", + "score": 0.93, + "mean": 0.93, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-r12-20260923T074850Z/023/traj.jsonl", + "fetched_at": "2026-10-26T06:55:16Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_019kav3egMrnGpeYf2ok68aw", + "name": "execute", + "input": { + "title": "Exploring the ROBOX repository structure", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar r = await machine.exec({ command: \"ls -la ~/ROBOX\" })\nconsole.log(r.stdout, r.stderr)" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@075141572885.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01WcAACMXQJruWeEowSjijYZ", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && echo '=== requirements.txt ===' && cat requirements.txt && echo '=== setup.py ===' && cat setup.py && echo '=== README.md ===' && cat README.md && echo '=== .gitignore ===' && cat .gitignore\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Reading requirements, setup.py, and README" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@075202899601.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01HDnW2Mdhq5qU6UjTB9iwLE", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && ls -la && echo '--- tree ---' && find . -maxdepth 3 -not -path '*/.git/*' | sort | head -200\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Listing repository files and directories" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@075230809215.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01KNkvE5y731CUnGxhtQ1JnX", + "name": "execute", + "input": { + "code": "var req = await machine.readFile({ path: \"/home/user/ROBOX/requirements.txt\" })\nvar setup = await machine.readFile({ path: \"/home/user/ROBOX/setup.py\" })\nvar readme = await machine.readFile({ path: \"/home/user/ROBOX/README.md\" })\nconsole.log(\"=== requirements.txt (raw) ===\")\nconsole.log(JSON.stringify(req))\nconsole.log(\"=== setup.py ===\")\nconsole.log(setup)", + "title": "Reading raw requirements.txt and setup.py content" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@075253337174.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_01BEnVzGKkR8UAUgdUsh52Bi", + "name": "execute", + "input": { + "code": "console.log(readme)", + "title": "Reading the README in full" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@075315551258.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_019bdQo8T1p9T47sCYmv4kBi", + "name": "execute", + "input": { + "code": "console.log(setup)\nvar r2 = await machine.exec({ command: \"cd ~/ROBOX && grep -rhoE '^[[:space:]]*(from|import)[[:space:]]+[A-Za-z0-9_\\\\.]+' --include='*.py' . | sed -E 's/^[[:space:]]*(from|import)[[:space:]]+//' | cut -d. -f1 | sort | uniq -c | sort -rn\" })\nconsole.log(r2.stdout, r2.stderr)", + "title": "Scanning all Python imports in the codebase" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@075344181305.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01KtZvqHkGdhLyBSNmb1obd8", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"which conda; conda --version; conda env list; echo '---'; ls /opt | head; ls ~/ | head -30; echo '---python---'; python3 --version; echo '--- pip cache/ offline? ---'; timeout 15 pip download --no-deps --dest /tmp/nettest six 2>&1 | tail -5\" , timeout: 90})\nconsole.log(r.stdout, r.stderr)", + "title": "Checking conda installation and network access" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@075415004469.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01HfrKmoEeuT5MkFqays9Dpn", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"~/miniconda3/bin/conda --version; ~/miniconda3/bin/conda env list; echo '=== ~/requirement.txt ==='; cat ~/requirement.txt; echo '=== ls ~/server ==='; ls ~/server | head\" , timeout: 120})\nconsole.log(r.stdout, r.stderr)", + "title": "Checking miniconda and the stray requirement.txt file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@075438803118.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_012GiWQDPf1LuJ6tg55p3U4X", + "name": "execute", + "input": { + "code": "console.log(\"SETUP.PY:\\n\" + setup.slice(0, 1200))\nconsole.log(\"\\n\\nREADME part1:\\n\" + readme.slice(0, 1800))", + "title": "Reviewing setup.py and README installation section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@075501155570.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_014rq4vpG5jHZEZDMnWHByZg", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd ~/ROBOX && grep -rhoE '^[[:space:]]*(from|import)[[:space:]]+[A-Za-z0-9_\\\\.]+' --include='*.py' . | sed -E 's/^[[:space:]]*(from|import)[[:space:]]+//' | sort -u` })\nconsole.log(r.stdout)", + "title": "Collecting the full list of imported modules" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@075526126970.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01HZCwBHbidUMNU89MTJVVrT", + "name": "execute", + "input": { + "code": "var i = readme.indexOf(\"Installation\", readme.indexOf(\"# Installation\"))\nconsole.log(\"=== README INSTALL SECTION ===\")\nconsole.log(readme.slice(readme.indexOf(\"# Installation\"), readme.indexOf(\"# Installation\") + 900))", + "title": "Reading the README installation instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@075601269561.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_0115X8MZ6MDoGnSeGUM4sBsA", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && grep -n -i -A 25 'installation' README.md | head -60\" })\nconsole.log(r.stdout)", + "title": "Extracting installation steps from README" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@075623945550.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01RkgFXDTjFc1PKNnsQqaZUe", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"df -h / /home 2>/dev/null; echo '--- gpu ---'; (nvidia-smi -L 2>&1 | head -3); echo '--- nproc/mem ---'; nproc; free -g | head -2\" })\nconsole.log(r.stdout)\nvar r2 = await machine.exec({ command: \"cd ~/ROBOX && grep -rn 'robomimic\\\\|robosuite' --include='*.py' . | grep -E 'import' | sort -u | head -60\" })\nconsole.log(r2.stdout)", + "title": "Checking disk, GPU, and robosuite/robomimic API usage" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@075647622423.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01TW3aQCXuWotoufUqVHRehp", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && grep -rn 'from bddl\\\\|import bddl\\\\|from transformers\\\\|import transformers\\\\|import gym\\\\|from gym\\\\|thop\\\\|huggingface_hub\\\\|import mujoco\\\\|from mujoco\\\\|einops\\\\|cloudpickle\\\\|from future\\\\|import future' --include='*.py' . | sort -u\" })\nconsole.log(r.stdout)", + "title": "Checking usage of bddl, transformers, gym, and other libraries" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@075723601128.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_012FZH2VAhi5rpxScR1XApN2", + "name": "execute", + "input": { + "code": "var py = `\nimport ast, os, sys, json\nroot = os.path.expanduser(\"~/ROBOX\")\nmods = {}\nfor dirpath, dirnames, filenames in os.walk(root):\n if \".git\" in dirpath: continue\n for fn in filenames:\n if not fn.endswith(\".py\"): continue\n p = os.path.join(dirpath, fn)\n try:\n tree = ast.parse(open(p, encoding=\"utf-8\", errors=\"replace\").read())\n except Exception as e:\n print(\"PARSE_FAIL\", p, e); continue\n for node in ast.walk(tree):\n if isinstance(node, ast.Import):\n for a in node.names:\n mods.setdefault(a.name.split(\".\")[0], set()).add(os.path.relpath(p, root))\n elif isinstance(node, ast.ImportFrom):\n if node.level and node.level > 0: continue\n if node.module:\n mods.setdefault(node.module.split(\".\")[0], set()).add(os.path.relpath(p, root))\nstd = set(sys.stdlib_module_names) if hasattr(sys, \"stdlib_module_names\") else set()\nlocal = {\"robox\", \"scripts\", \"benchmark_scripts\", \"templates\", \"init_path\", \"utils\", \"benchmark_utils\"}\nthird = {k: sorted(v)[:3] for k, v in sorted(mods.items()) if k not in std and k not in local and not k.startswith(\"_\")}\nprint(json.dumps(third, indent=1))\n`\nawait machine.writeToFile({ text: py, path: \"/tmp/scan_imports.py\", overwrite: true })\nvar r = await machine.exec({ command: \"python3 /tmp/scan_imports.py\" })\nconsole.log(r.stdout, r.stderr)", + "title": "Parsing all source files to extract third-party imports" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@075753277815.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_016yJnVZbXe6kxTPGqett8ya", + "name": "execute", + "input": { + "code": "var deps = {\n external: [\"Pillow\",\"bddl\",\"cloudpickle\",\"opencv-python\",\"easydict\",\"einops\",\"future\",\"gym\",\"h5py\",\"huggingface_hub\",\"hydra-core\",\"imageio\",\"matplotlib\",\"mujoco\",\"numpy\",\"omegaconf\",\"robomimic\",\"robosuite\",\"termcolor\",\"thop\",\"torch\",\"torchvision\",\"tqdm\",\"transformers\",\"wandb\",\"pyyaml\"],\n notes: \"ROBOX = renamed LIBERO benchmark. requirements.txt is corrupted mojibake about SLAM/nav-stack -> unusable. README says: conda create -n robox python=3.9; pip install -r requirements.txt; pip install -e . Code needs robosuite<=1.4 (imports robosuite.environments.manipulation.single_arm_env.SingleArmEnv) and robomimic. No GPU on machine -> CPU torch.\"\n}\nvar r = await machine.exec({ command: \"cd ~/ROBOX && cat robox/robox/utils/video_utils.py | head -40; echo '=== env init ==='; sed -n '1,40p' robox/robox/envs/__init__.py; echo '=== robox/__init__ ==='; sed -n '1,60p' robox/robox/__init__.py\" })\nconsole.log(r.stdout)", + "title": "Inspecting video utilities and package initialization" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@075830071301.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01TAdJcQKpo9NDzh1Q6hauyK", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"~/miniconda3/bin/conda env list; ls ~/miniconda3/envs 2>/dev/null\" })\nconsole.log(\"EXISTING ENVS:\\n\" + r.stdout + r.stderr)\nvar r2 = await machine.exec({ command: \"~/miniconda3/bin/conda create -y -n robox python=3.9 2>&1 | tail -25\", timeout: 900 })\nconsole.log(\"CREATE:\\n\" + r2.stdout + r2.stderr)", + "title": "Creating the robox conda environment with Python 3.9" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@075900992300.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_014XuS1DqepizpcJK7a2z1gA", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"~/miniconda3/bin/conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main 2>&1 | tail -3; ~/miniconda3/bin/conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r 2>&1 | tail -3\", timeout: 300 })\nconsole.log(r.stdout + r.stderr)\nvar r2 = await machine.exec({ command: \"~/miniconda3/bin/conda create -y -n robox python=3.9 2>&1 | tail -20\", timeout: 1200 })\nconsole.log(r2.stdout + r2.stderr)", + "title": "Accepting conda terms and creating the environment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@075938622650.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_01ChvTu7esTryHiry2ywHwC6", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"df -h /home /tmp | cat; echo '--- env python ---'; ~/miniconda3/envs/robox/bin/python --version; echo '--- transformers usage ---'; cd ~/ROBOX && grep -rn 'transformers' --include='*.py' .; echo '--- gym usage ---'; grep -rn 'gym\\\\.' --include='*.py' . | head -20\" })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking disk space and library usage details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@080020828665.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_015nSYKn2DzhndT43sF4Lpsm", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"nvidia-smi -L 2>&1 | head -3; ls /dev/nvidia* 2>&1 | head -3; echo '--- libGL ---'; ldconfig -p | grep -E 'libGL\\\\.so|libEGL|libOSMesa' | head; echo '--- gcc/cmake ---'; which gcc cmake; echo '--- sudo ---'; sudo -n true 2>&1 | head -2\" })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking for GPU, graphics libraries, and build tools" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@080058472139.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01QxN4CQHuRd7WLbtD2E45GM", + "name": "execute", + "input": { + "code": "var PIP = \"~/miniconda3/envs/robox/bin/pip\"\nawait machine.writeToFile({ text: \"numpy<2\\n\", path: \"/tmp/constraints.txt\", overwrite: true })\nvar r = await machine.exec({ command: `${PIP} install --upgrade pip setuptools wheel 2>&1 | tail -4 && ${PIP} install -c /tmp/constraints.txt \"numpy==1.24.4\" cmake 2>&1 | tail -6`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing base build tooling and NumPy" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@080131567847.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_01Rd3LA5gexHVv2gvrQeY8HD", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `${PIP} install -c /tmp/constraints.txt --index-url https://download.pytorch.org/whl/cpu \"torch==2.4.1\" \"torchvision==0.19.1\" 2>&1 | tail -8`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing PyTorch and torchvision (CPU builds)" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@080211821313.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_01Hu4PX38Kv7zdhFr7i7cCKS", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && git log --oneline | head -20; echo '--- status ---'; git status --short | head -20; echo '--- req history ---'; git log --oneline -- requirements.txt | head; echo '--- grep version hints ---'; grep -rn 'robosuite' --include='*.md' . | head; grep -rn '1\\\\.4\\\\|version' --include='*.py' robox/robox/__init__.py | head\" })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking git history for the original requirements file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@080241282053.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01AHys3Lofs5SGGf9yMPhYGy", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `${PIP} install -c /tmp/constraints.txt \"robosuite==1.4.1\" 2>&1 | tail -12`, timeout: 1800 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing robosuite 1.4.1" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@080316673942.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_016nFDsYYeWyXpSx4YFGGNx5", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `${PIP} download --no-deps --no-binary :none: -d /tmp/mjwheel \"mujoco==2.3.0\" 2>&1 | tail -8; echo '=== index versions ==='; ${PIP} index versions mujoco 2>&1 | head -5; echo '=== robosuite req ==='; ${PIP} download --no-deps -d /tmp/rs \"robosuite==1.4.1\" 2>&1 | tail -3`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Diagnosing the mujoco wheel installation failure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@080344246930.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_01WQpMBmzriiihoTaMjdBFFp", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp/rs && ${PIP.replace('/pip','/python')} -c \"\nimport zipfile,re\nz=zipfile.ZipFile('robosuite-1.4.1-py3-none-any.whl')\nn=[x for x in z.namelist() if x.endswith('METADATA')][0]\nm=z.read(n).decode()\nprint('\\\\n'.join([l for l in m.split('\\\\n') if l.startswith('Requires') or l.startswith('Python-Requires') or l.startswith('Requires-Python')]))\n\"`, timeout: 300 })\nconsole.log(r.stdout + r.stderr)", + "title": "Inspecting robosuite's declared dependencies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@080413087108.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_016bQ1QNv925AHoqXuYkfrtM", + "name": "execute", + "input": { + "code": "await machine.writeToFile({ text: \"numpy<2\\nmujoco<3\\n\", path: \"/tmp/constraints.txt\", overwrite: true })\nvar r = await machine.exec({ command: `${PIP} install -c /tmp/constraints.txt \"robosuite==1.4.1\" 2>&1 | tail -8`, timeout: 1800 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing robosuite with a compatible mujoco version" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@080504633928.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01KLcTHYkYb1zBqEGhz1MdGk", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `${PIP} install -c /tmp/constraints.txt \"robomimic==0.2.0\" 2>&1 | tail -10`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing robomimic" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@080538044630.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_014fb7AM1pHPW9S1k3uhiDUr", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && which cmake && ${PIP} install -c /tmp/constraints.txt \"robomimic==0.2.0\" 2>&1 | tail -12`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Retrying robomimic install with cmake available on PATH" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@080609043379.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01RsJZXTYUojr92LjbQNEaQi", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `~/miniconda3/envs/robox/bin/cmake --version; echo \"exit=$?\"` , timeout: 120})\nconsole.log(r.stdout + \"\\nSTDERR:\\n\" + r.stderr)", + "title": "Testing the cmake installation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@080635316415.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01Q3oAucawpCf53AFyBTPh1b", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install \"egl_probe\" 2>&1 | tail -45`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Capturing the egl_probe build error details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@080701448310.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_012Dung5oFHMqEEpZQK1gfxp", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && rm -rf eglsrc && mkdir eglsrc && cd eglsrc && ${PIP} download --no-deps --no-binary :all: egl_probe -d . 2>&1 | tail -2 && ls && tar xzf *.tar.gz && ls */ && echo '=== setup.py ===' && sed -n '1,60p' egl_probe-*/setup.py && echo '=== CMakeLists ===' && find egl_probe-* -name 'CMakeLists.txt' -exec head -5 {} \\\\;`, timeout: 600 })\nconsole.log(r.stdout + r.stderr)", + "title": "Inspecting the egl_probe source build requirements" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@080739359447.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_01P8Tvxcrb89ju1dQa79UZZg", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `which make gcc g++; echo '---'; ${PIP} install \"cmake<4\" 2>&1 | tail -3`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing an older cmake compatible with the build" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@080808578368.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_01LqSneLtSbKGL3wtV7LCV2w", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install -c /tmp/constraints.txt \"robomimic==0.2.0\" 2>&1 | tail -8`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing robomimic again now that cmake works" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@080832507206.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_01Qo7JcF2XwZ61ggoyVrnf8R", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp/eglsrc/egl_probe-1.0.2 && export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ~/miniconda3/envs/robox/bin/python setup.py build 2>&1 | tail -40`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Building egl_probe manually to see the real error" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@080902132400.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_01T53dSYnxyXvsz6qvfKfWZ4", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install --no-build-isolation \"egl_probe\" 2>&1 | tail -5`, timeout: 1200 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing egl_probe without build isolation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@080938646112.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_01LFthp5Xn1LzZyoWqD9hqvj", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install -c /tmp/constraints.txt \"robomimic==0.2.0\" 2>&1 | tail -6`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing robomimic with its dependencies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@081006282404.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01WezzfhwCpVKCUUDBZBmaE6", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install -c /tmp/constraints.txt \"bddl==1.0.1\" \"gym==0.25.2\" easydict einops future cloudpickle hydra-core matplotlib thop transformers wandb pyyaml huggingface_hub 2>&1 | tail -12`, timeout: 2400 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing remaining project dependencies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@081115964100.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_01WYgAoEcBuj4MLm8AqB4Yvm", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/ROBOX && grep -n -A 20 'install_requires\\\\|setup(' setup.py | head -40\" })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking setup.py declared requirements before install" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@081141761431.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_01WMZqVm2yDTghCE4mVRz6R2", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd ~/ROBOX && export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install -c /tmp/constraints.txt -e . 2>&1 | tail -8`, timeout: 1800 })\nconsole.log(r.stdout + r.stderr)", + "title": "Installing the robox package in editable mode" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@081203616900.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_014cM8WSe1vCrdqJ7jgcyanb", + "name": "execute", + "input": { + "code": "var verify = `\nimport importlib, traceback, sys\nthird = [\"numpy\",\"torch\",\"torchvision\",\"robosuite\",\"robomimic\",\"mujoco\",\"cv2\",\"PIL\",\"h5py\",\"hydra\",\n \"omegaconf\",\"yaml\",\"easydict\",\"einops\",\"future\",\"gym\",\"huggingface_hub\",\"imageio\",\"imageio_ffmpeg\",\n \"matplotlib\",\"termcolor\",\"thop\",\"tqdm\",\"transformers\",\"wandb\",\"cloudpickle\",\"bddl\",\"egl_probe\"]\nok, bad = [], []\nfor m in third:\n try:\n mod = importlib.import_module(m)\n ok.append(\"%s %s\" % (m, getattr(mod, \"__version__\", \"\")))\n except Exception as e:\n bad.append(\"%s -> %r\" % (m, e))\nprint(\"THIRD-PARTY OK:\", len(ok))\nfor x in ok: print(\" \", x)\nprint(\"THIRD-PARTY FAILED:\", len(bad))\nfor x in bad: print(\" \", x)\n`\nawait machine.writeToFile({ text: verify, path: \"/tmp/verify_imports.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd ~/ROBOX && ~/miniconda3/envs/robox/bin/python /tmp/verify_imports.py 2>&1 | tail -45`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Verifying all third-party imports in the new environment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@081237392988.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_01RRxXHrm4XqjgRjGpUxoTLM", + "name": "execute", + "input": { + "code": "var verify2 = `\nimport importlib, traceback\nmods = [\"robox\", \"robox.robox\", \"robox.robox.envs\", \"robox.robox.benchmark\", \"robox.robox.utils.bddl_generation_utils\",\n \"robox.robox.utils.task_generation_utils\", \"robox.robox.utils.download_utils\", \"robox.robox.utils.video_utils\",\n \"robox.continual\", \"robox.continual.algos\", \"robox.continual.models\", \"robox.continual.datasets\",\n \"robox.continual.utils\", \"robox.continual.metric\", \"robox.continual.main\", \"robox.continual.evaluate\"]\nfor m in mods:\n try:\n importlib.import_module(m); print(\"OK \", m)\n except Exception as e:\n print(\"FAIL\", m, \"->\", repr(e)); traceback.print_exc()\nfrom robox.robox.envs import OffScreenRenderEnv\nfrom robox.robox import benchmark, get_robox_path\nprint(\"benchmark suites:\", sorted(benchmark.get_benchmark_dict().keys()))\n`\nawait machine.writeToFile({ text: verify2, path: \"/tmp/verify_repo.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd ~/ROBOX && ~/miniconda3/envs/robox/bin/python /tmp/verify_repo.py 2>&1 | grep -v Warning | tail -40`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Verifying the project's own modules import correctly" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@081306343724.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_016YDtFcDhPVGKA57xU4vysM", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `ls ~/ROBOX/robox/ | head; echo '--- init files ---'; ls -la ~/ROBOX/robox/__init__.py ~/ROBOX/robox/robox/__init__.py ~/ROBOX/robox/continual/__init__.py 2>&1; echo '--- pip show ---'; ${PIP} show -f robox 2>&1 | head -20; echo '--- site-packages editable ---'; ls ~/miniconda3/envs/robox/lib/python3.9/site-packages | grep -i -E 'robox|editable'`, timeout: 300 })\nconsole.log(r.stdout + r.stderr)", + "title": "Diagnosing why the robox package is not importable" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@081333484540.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_013PQVhzNZZVeNEYwcD9r8yk", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cat ~/miniconda3/envs/robox/lib/python3.9/site-packages/__editable___robox_0_1_0_finder.py | head -20; echo '=== pth ==='; cat ~/miniconda3/envs/robox/lib/python3.9/site-packages/__editable__.robox-0.1.0.pth; echo; echo '=== import from repo root ==='; cd ~/ROBOX && ~/miniconda3/envs/robox/bin/python -c \"\nimport robox, robox.robox\nprint('robox pkg path:', robox.__path__)\nfrom robox.robox import benchmark, get_robox_path\nprint('suites:', sorted(benchmark.get_benchmark_dict().keys()))\n\" 2>&1 | tail -20`, timeout: 600 })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking the editable install mapping and repo-root import" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@081436042610.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_01GUfu9XHWtWRiAp7f955o3m", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `sed -n '1,110p' ~/ROBOX/robox/robox/__init__.py` })\nconsole.log(r.stdout + r.stderr)", + "title": "Reading the package initialization and config logic" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@081516186260.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_011Bdfc3RtFkQuwixW4sNp17", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd ~/ROBOX && echo \"N\" | ~/miniconda3/envs/robox/bin/python -c \"import robox.robox\" 2>&1 | tail -12; echo '=== config ==='; cat ~/.robox/config.yaml`, timeout: 600 })\nconsole.log(r.stdout + r.stderr)", + "title": "Creating the default robox config file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@081538786753.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_013QLMM8yqU514CMQLho8DAw", + "name": "execute", + "input": { + "code": "await machine.writeToFile({ text: \"\", path: \"/home/user/ROBOX/robox/__init__.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd ~/ROBOX && export PATH=$HOME/miniconda3/envs/robox/bin:$PATH && ${PIP} install -c /tmp/constraints.txt -e . 2>&1 | tail -4; echo '=== mapping ==='; grep -A 8 'MAPPING' ~/miniconda3/envs/robox/lib/python3.9/site-packages/__editable___robox_0_1_0_finder.py | head -12`, timeout: 1200 })\nconsole.log(r.stdout + r.stderr)", + "title": "Adding the missing package init file and reinstalling" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@081607039053.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": { + "id": "toolu_bdrk_01QzjQccRebH92uqDhTfjTAm", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && ~/miniconda3/envs/robox/bin/python /tmp/verify_repo.py 2>&1 | grep -E '^(OK|FAIL|benchmark)' `, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Verifying project modules import from any directory" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_48_20260923@081632431545.png", + "osworld_action": "WAIT" + }, + { + "step_num": 49, + "action": { + "id": "toolu_bdrk_01RtgthvnYzD5k7SUozq7T71", + "name": "execute", + "input": { + "code": "var smoke = `\nimport os, sys\nos.environ.setdefault(\"MUJOCO_GL\", os.environ.get(\"MJGL\", \"egl\"))\nimport numpy as np\nfrom robox.robox import benchmark, get_robox_path\nfrom robox.robox.envs import OffScreenRenderEnv\n\nbd = benchmark.get_benchmark_dict()\nsuite = bd[\"robox_spatial\"]()\ntask = suite.get_task(0)\nbddl_file = os.path.join(get_robox_path(\"bddl_files\"), task.problem_folder, task.bddl_file)\nprint(\"task:\", task.name, \"|\", task.language)\nprint(\"bddl:\", bddl_file, \"exists:\", os.path.exists(bddl_file))\n\nenv = OffScreenRenderEnv(bddl_file_name=bddl_file, camera_heights=84, camera_widths=84)\nenv.seed(0)\nobs = env.reset()\nfor _ in range(3):\n obs, reward, done, info = env.step(np.zeros(7))\nprint(\"obs keys:\", sorted([k for k in obs.keys()])[:10])\nprint(\"agentview_image:\", obs[\"agentview_image\"].shape, obs[\"agentview_image\"].dtype)\nenv.close()\nprint(\"SMOKE TEST OK with MUJOCO_GL=\" + os.environ[\"MUJOCO_GL\"])\n`\nawait machine.writeToFile({ text: smoke, path: \"/tmp/smoke_env.py\", overwrite: true })\nvar r = await machine.exec({ command: `ldconfig -p | grep -iE 'osmesa' | head -3; cd /tmp && MJGL=egl ~/miniconda3/envs/robox/bin/python /tmp/smoke_env.py 2>&1 | tail -20`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Running a simulation smoke test with EGL rendering" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_49_20260923@081728675875.png", + "osworld_action": "WAIT" + }, + { + "step_num": 50, + "action": { + "id": "toolu_bdrk_017vd9Ny6ow9jk3MLcYANzyn", + "name": "execute", + "input": { + "code": "var sweep = `\nimport pkgutil, importlib, traceback\nimport robox\nfails = []\ncount = 0\nfor mod in pkgutil.walk_packages(robox.__path__, prefix=\"robox.\"):\n name = mod.name\n if any(s in name for s in (\".problems.\", \"wandb\")):\n pass\n try:\n importlib.import_module(name); count += 1\n except Exception as e:\n fails.append((name, repr(e)))\nprint(\"modules imported OK:\", count)\nprint(\"failures:\", len(fails))\nfor n, e in fails[:25]:\n print(\" FAIL\", n, \"->\", e)\n`\nawait machine.writeToFile({ text: sweep, path: \"/tmp/sweep.py\", overwrite: true })\nvar r = await machine.exec({ command: `echo y | ~/miniconda3/envs/robox/bin/python ~/miniconda3/envs/robox/lib/python3.9/site-packages/robosuite/scripts/setup_macros.py 2>&1 | tail -3; echo '=== sweep ==='; cd /tmp && MUJOCO_GL=egl ~/miniconda3/envs/robox/bin/python /tmp/sweep.py 2>&1 | grep -E 'modules imported|failures|FAIL'; echo '=== pip check ==='; ${PIP} check 2>&1 | tail -6`, timeout: 1200 })\nconsole.log(r.stdout + r.stderr)", + "title": "Sweeping every project module and checking dependency consistency" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_50_20260923@081819732528.png", + "osworld_action": "WAIT" + }, + { + "step_num": 51, + "action": { + "id": "toolu_bdrk_01BsV73HKMUThHD3qVzaCq5F", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `grep -n 'ProjectDefaultLogger' -r ~/ROBOX --include='*.py'; echo '=== file head ==='; sed -n '1,40p' ~/ROBOX/robox/robox/utils/log_utils.py` })\nconsole.log(r.stdout + r.stderr)", + "title": "Investigating the single failing module" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_51_20260923@081843132363.png", + "osworld_action": "WAIT" + }, + { + "step_num": 52, + "action": { + "id": "toolu_bdrk_01CQZHaQNrnXqfXPr4BnBvw9", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `grep -rn 'log_utils' ~/ROBOX --include='*.py' | grep -v 'utils/log_utils.py:' | head; echo '=== bashrc conda init? ==='; grep -n 'conda initialize' -A 2 ~/.bashrc | head -6; echo \"exit=$?\"; echo '=== SimularFiles ==='; ls -d ~/SimularFiles ~/SimularFiles/artifacts 2>&1 | head -3` })\nconsole.log(r.stdout + r.stderr)", + "title": "Checking log_utils usage, conda shell setup, and output folder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_52_20260923@081918283248.png", + "osworld_action": "WAIT" + }, + { + "step_num": 53, + "action": { + "id": "toolu_bdrk_01SnYdw7uMxxSzhs9NkNpbuB", + "name": "execute", + "input": { + "code": "var t = `\nimport torch, robomimic.models.base_nets as bn\nfrom robomimic.models.obs_nets import ObservationEncoder\nimport einops, thop\nm = bn.SpatialSoftmax(input_shape=[64, 7, 7], num_kp=32)\nprint(\"SpatialSoftmax out:\", tuple(m(torch.zeros(2, 64, 7, 7)).shape))\nr18 = bn.ResNet18Conv(input_channel=3, pretrained=False)\nprint(\"ResNet18Conv out:\", tuple(r18(torch.zeros(1, 3, 84, 84)).shape))\nfrom robox.continual.models import get_policy_class\nfrom robox.continual.algos import get_algo_class\nprint(\"policy class:\", get_policy_class(\"BCTransformerPolicy\").__name__)\nprint(\"algo class:\", get_algo_class(\"base\").__name__)\nprint(\"TRAIN STACK OK\")\n`\nawait machine.writeToFile({ text: t, path: \"/tmp/train_stack.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd /tmp && MUJOCO_GL=egl ~/miniconda3/envs/robox/bin/python /tmp/train_stack.py 2>&1 | tail -12; echo '=== hydra config composition ==='; cd ~/ROBOX && timeout 180 ~/miniconda3/envs/robox/bin/python robox/continual/main.py --cfg job 2>&1 | head -25`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Testing the training stack and hydra configuration" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_53_20260923@082013347759.png", + "osworld_action": "WAIT" + }, + { + "step_num": 54, + "action": { + "id": "toolu_bdrk_018cDajN3bWDDQijcAhRYCAH", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `~/miniconda3/envs/robox/bin/pip freeze --exclude-editable > /tmp/freeze.txt; wc -l /tmp/freeze.txt; grep -iE '^(numpy|torch|torchvision|robosuite|robomimic|mujoco|bddl|gym|transformers|hydra-core|opencv-python|h5py|easydict|einops|future|cloudpickle|matplotlib|thop|wandb|PyYAML|imageio|imageio-ffmpeg|termcolor|tqdm|huggingface-hub|Pillow|egl-probe|numba|scipy)==' /tmp/freeze.txt`, timeout: 300 })\nconsole.log(r.stdout + r.stderr)", + "title": "Capturing the resolved package versions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_54_20260923@082042392098.png", + "osworld_action": "WAIT" + }, + { + "step_num": 55, + "action": { + "id": "toolu_bdrk_01LGWxGLEEo9Y4nNbesictKQ", + "name": "execute", + "input": { + "code": "var reqFile = `# Resolved dependency list for ROBOX (generated after inspecting the source imports).\n# The original requirements.txt in this repo is corrupted (mojibake text about an\n# unrelated SLAM / nav-stack project) and cannot be installed.\n#\n# Verified working in: conda env \"robox\" (python 3.9)\n# conda create -n robox python=3.9 && conda activate robox\n# pip install -r requirements-resolved.txt\n# pip install -e .\n#\n# Notes:\n# * robosuite must stay on the 1.4.x line: the code imports\n# robosuite.environments.manipulation.single_arm_env.SingleArmEnv (removed in 1.5).\n# * mujoco must stay < 3 (robosuite 1.4 API) and numpy < 2.\n# * torch/torchvision below are the CPU builds (this machine has no GPU); for a\n# CUDA machine install the matching CUDA wheels instead.\n# * egl_probe (robomimic dependency) needs cmake<4 + a C/C++ compiler and must be\n# installed with --no-build-isolation.\n\nnumpy==1.24.4\nscipy==1.13.1\ntorch==2.4.1\ntorchvision==0.19.1\nrobosuite==1.4.1\nmujoco==2.3.7\nrobomimic==0.2.0\negl_probe==1.0.2\nbddl==1.0.1\ngym==0.25.2\nh5py==3.14.0\nopencv-python==4.11.0.86\nPillow==11.3.0\nimageio==2.37.2\nimageio-ffmpeg==0.6.0\nmatplotlib==3.9.4\nhydra-core==1.3.7\nomegaconf==2.3.0\neasydict==1.13\neinops==0.8.2\nfuture==1.0.0\ncloudpickle==3.1.2\ntermcolor==3.1.0\nthop==0.1.1.post2209072238\ntqdm==4.70.1\ntransformers==4.57.6\nhuggingface_hub==0.36.0\nwandb==0.26.1\nPyYAML==6.0.3\n`\nawait machine.writeToFile({ text: reqFile, path: \"/home/user/ROBOX/requirements-resolved.txt\", overwrite: true })\nvar r = await machine.exec({ command: `~/miniconda3/bin/conda env list; echo '=== activate + full check ==='; bash -ic 'conda activate robox && python -c \"\nimport os; os.environ[\\\\\"MUJOCO_GL\\\\\"]=\\\\\"egl\\\\\"\nimport robox, robosuite, robomimic, mujoco, torch, bddl, transformers, cv2, h5py, hydra, wandb\nfrom robox.robox import benchmark\nfrom robox.robox.envs import OffScreenRenderEnv\nprint(\\\\\"python\\\\\", __import__(\\\\\"sys\\\\\").version.split()[0], \\\\\"| env:\\\\\", os.environ.get(\\\\\"CONDA_DEFAULT_ENV\\\\\"))\nprint(\\\\\"robox suites:\\\\\", sorted(benchmark.get_benchmark_dict().keys()))\nprint(\\\\\"ALL GOOD\\\\\")\n\"' 2>&1 | tail -8`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Writing a working requirements file and final environment check" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_55_20260923@082118551605.png", + "osworld_action": "WAIT" + }, + { + "step_num": 56, + "action": { + "id": "toolu_bdrk_018PksDQ7FAMJKQptLtiaNrd", + "name": "execute", + "input": { + "code": "var finalCheck = `\nimport ast, os, sys, importlib\nroot = os.path.expanduser(\"~/ROBOX\")\nfound = {}\nfor dirpath, dirnames, filenames in os.walk(root):\n for fn in filenames:\n if not fn.endswith(\".py\"): continue\n p = os.path.join(dirpath, fn)\n try: tree = ast.parse(open(p, encoding=\"utf-8\", errors=\"replace\").read())\n except Exception: continue\n for node in ast.walk(tree):\n if isinstance(node, ast.Import):\n for a in node.names: found.setdefault(a.name.split(\".\")[0], set()).add(os.path.relpath(p, root))\n elif isinstance(node, ast.ImportFrom):\n if not node.level and node.module: found.setdefault(node.module.split(\".\")[0], set()).add(os.path.relpath(p, root))\nstd = set(sys.stdlib_module_names)\nlocal = {\"robox\",\"scripts\",\"benchmark_scripts\",\"templates\",\"init_path\",\"utils\",\"benchmark_utils\",\"setup\"}\nmissing = []\nchecked = 0\nfor m in sorted(found):\n if m in std or m in local or m.startswith(\"_\"): continue\n checked += 1\n try: importlib.import_module(m)\n except Exception as e: missing.append((m, sorted(found[m])[:2], repr(e)[:90]))\nprint(\"third-party modules referenced by repo:\", checked)\nprint(\"NOT importable in env:\", len(missing))\nfor m, files, e in missing: print(\" MISSING\", m, \"used in\", files, \"->\", e)\n`\nawait machine.writeToFile({ text: finalCheck, path: \"/tmp/final_check.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd /tmp && MUJOCO_GL=egl ~/miniconda3/envs/robox/bin/python /tmp/final_check.py 2>&1 | grep -E 'third-party|NOT importable|MISSING'`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Cross-checking every imported module against the environment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_56_20260923@082153720362.png", + "osworld_action": "WAIT" + }, + { + "step_num": 57, + "action": { + "id": "toolu_bdrk_013T2KKsWA7PfpVQa8MwN23B", + "name": "execute", + "input": { + "code": "var finalCheck2 = finalCheck.replace('std = set(sys.stdlib_module_names)',\n 'std = set(getattr(sys, \"stdlib_module_names\", None) or list(sys.builtin_module_names) + [\"os\",\"sys\",\"re\",\"json\",\"time\",\"math\",\"random\",\"copy\",\"glob\",\"shutil\",\"pathlib\",\"typing\",\"argparse\",\"logging\",\"subprocess\",\"collections\",\"functools\",\"itertools\",\"abc\",\"enum\",\"dataclasses\",\"datetime\",\"pickle\",\"pprint\",\"hashlib\",\"socket\",\"struct\",\"threading\",\"multiprocessing\",\"queue\",\"warnings\",\"traceback\",\"inspect\",\"importlib\",\"pkgutil\",\"tempfile\",\"textwrap\",\"string\",\"io\",\"csv\",\"base64\",\"zipfile\",\"tarfile\",\"urllib\",\"xml\",\"unittest\",\"ctypes\",\"gc\",\"pdb\",\"platform\",\"signal\",\"stat\",\"uuid\",\"weakref\",\"contextlib\",\"operator\",\"bisect\",\"heapq\",\"asyncio\",\"concurrent\",\"distutils\",\"site\",\"sysconfig\",\"atexit\",\"codecs\",\"errno\",\"fnmatch\",\"getpass\",\"gzip\",\"http\",\"imp\",\"numbers\",\"secrets\",\"select\",\"shlex\",\"statistics\",\"termios\",\"tty\",\"types\",\"wave\",\"webbrowser\",\"setuptools\",\"pip\",\"pkg_resources\",\"future_builtins\"])')\nawait machine.writeToFile({ text: finalCheck2, path: \"/tmp/final_check2.py\", overwrite: true })\nvar r = await machine.exec({ command: `cd /tmp && MUJOCO_GL=egl ~/miniconda3/envs/robox/bin/python /tmp/final_check2.py 2>&1 | grep -E 'third-party|NOT importable|MISSING'`, timeout: 900 })\nconsole.log(r.stdout + r.stderr)", + "title": "Re-running the full dependency coverage check" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_57_20260923@082226138305.png", + "osworld_action": "WAIT" + }, + { + "step_num": 58, + "action": { + "id": "toolu_bdrk_01VK6QodvUSNmyeogUvtDUBr", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `echo '=== conda envs ==='; ~/miniconda3/bin/conda env list; echo '=== files I created ==='; ls -la ~/ROBOX/robox/__init__.py ~/ROBOX/requirements-resolved.txt ~/.robox/config.yaml; ls -d ~/ROBOX/robox.egg-info; echo '=== original requirements.txt untouched ==='; ls -la ~/ROBOX/requirements.txt; md5sum ~/ROBOX/requirements.txt; echo '=== package count in env ==='; ~/miniconda3/envs/robox/bin/pip list 2>/dev/null | wc -l; echo '=== editable robox ==='; ~/miniconda3/envs/robox/bin/pip list 2>/dev/null | grep -i robo`, timeout: 600 })\nconsole.log(r.stdout + r.stderr)", + "title": "Confirming final environment state and created files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_58_20260923@082254096538.png", + "osworld_action": "WAIT" + }, + { + "step_num": 59, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_59_20260923@082324369739.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/026.json b/demo/data/official/sai_traj/026.json new file mode 100644 index 0000000..bbfdcd0 --- /dev/null +++ b/demo/data/official/sai_traj/026.json @@ -0,0 +1,2058 @@ +{ + "task": "026", + "run": "v21-ref5-rest-20260923T103950Z", + "score": 0.5, + "mean": 0.5, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref5-rest-20260923T103950Z/026/traj.jsonl", + "fetched_at": "2026-10-26T06:55:17Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01MDMPEQQoxhbRwjg4jKoSeJ", + "name": "execute", + "input": { + "code": "var machines = await listMachines()\nconsole.log(JSON.stringify(machines, null, 2))\nvar machine = getMachine(machines[0].id)\nvar windows = await machine.listAppWindows()\nconsole.log(JSON.stringify(windows, null, 2))\n", + "title": "Listing machines and open windows" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@110818074821.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01PNXFEgVT8MYkMHLH7aShtX", + "name": "execute", + "input": { + "code": "var chrome = await machine.getAppWindow('Sign in · GitLab - Google Chrome')\nawait chrome.activate()\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Taking a screenshot of the current screen" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@110830536793.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01F9aLc3SqBuhd8Zjq9kzN79", + "name": "execute", + "input": { + "code": "// Dismiss the \"Restore pages?\" bubble\nawait machine.click({ x: 1890, y: 139 })\nawait wait({ waitTime: 1 })\n// Switch to TeamChat tab\nawait machine.click({ x: 220, y: 47 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Opening the TeamChat tab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@110846247657.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_015QC2fSfJaCGygNDnYEGWwr", + "name": "execute", + "input": { + "code": "// Allow notifications as requested\nawait machine.click({ x: 453, y: 249 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Allowing notifications for TeamChat" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@110901369330.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_015VbK79mauF2AUWMDGQwgws", + "name": "execute", + "input": { + "code": "await machine.click({ x: 235, y: 615 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Opening Rachel's direct message conversation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@110915866720.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_0188hhfAhF5LWEvNYwHvjAXJ", + "name": "execute", + "input": { + "code": "var snap = await chrome.snapshot()\nconsole.log(snap.snapshot.outline())\n", + "title": "Reading the full TeamChat conversation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@110930361328.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01U7dSk1ewXnwNm4Rn1MejVL", + "name": "execute", + "input": { + "code": "var pc = await machine.pageContent()\nconsole.log(pc.text.substring(0, 8000))\n", + "title": "Extracting the conversation text from the page" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@110946395414.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_014vsiWkijG639UZBjvCDpK2", + "name": "execute", + "input": { + "code": "console.log(typeof pc, Object.keys(pc))\nconsole.log(JSON.stringify(pc.text).substring(0, 5000))\n", + "title": "Checking the extracted page content structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@110956025397.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_017JJTYV8GxZHNNGiQoDXVg6", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(1, 440))\n", + "title": "Reading the conversation tree content" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@111005254642.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_01B6QhdiT8okrSZGMSydspif", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(100, 199))\n", + "title": "Reading more of the conversation content" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@111018791375.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01NNcx8ruySDgTpP33GoCtUC", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(199, 298))\n", + "title": "Continuing to read Rachel's instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@111028299528.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_019TPdYe9mRc27M1ruhF6hnU", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(120, 175))\n", + "title": "Reading the earlier part of Rachel's messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@111040579743.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_018APvsrfAiebBVWM16jR2in", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(175, 215))\n", + "title": "Reading the rest of Rachel's instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@111050640321.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01HsFjkna57GVBsXXLJrxaib", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(122, 160))\n", + "title": "Reading the beginning of the conversation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@111101353429.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01QCEqBGeHzikHgRi1rUN9qh", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(160, 205))\n", + "title": "Reading Rachel's task instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@111110574292.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01A17CT1XwnDbzHoDu8fTJax", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(122, 162))\n", + "title": "Reading the first messages with file attachments" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@111119738969.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01QDNnvjMUw3u7oXW6zaTQj3", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(162, 202))\n", + "title": "Reading the detailed task instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@111128952969.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01GLyV2k2cgssBHNuDGj2bKZ", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(123, 160))\n", + "title": "Reading the file attachment messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@111139682239.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_01EujV4jLrwh2sAgk9Ksy9ec", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(162, 200))\n", + "title": "Reading Rachel's task list message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@111148714386.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_01Tod1N6jmC5z91bd8UaK7Sj", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(124, 158))\n", + "title": "Reading Rachel's first messages and attachments" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@111158227325.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01Xp1DPqEnkSFQRwkLZuvAYR", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(166, 200))\n", + "title": "Reading Rachel's detailed task requirements" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@111207484821.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_01E4F84rAydLm2oC1LUHsmC3", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(200, 212))\nconsole.log(\"=====\")\nconsole.log(pc.text.read(127, 156))\n", + "title": "Reading remaining messages and attachment names" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@111216995619.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_019bTu571Q1SFAUvDoEztUhj", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(168, 198))\n", + "title": "Reading Rachel's model and task instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@111227435752.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01H35C2WPjAnBZUp1G1VTriZ", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(127, 155))\n", + "title": "Reading Rachel's first message and attached files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@111236813662.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_014k6kg92PRx6MkqJqd7XBCE", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(171, 196))\n", + "title": "Reading Rachel's model choice and task list" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@111247853725.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_014c8tDnA3amN5BHP7Qmnfm2", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(128, 154))\n", + "title": "Reading the attached files list" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@111258947128.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01GVGE3XsuGMjoG58ZN3ozTw", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(173, 195))\n", + "title": "Reading Rachel's model recommendation and rules" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@111308244587.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01Y1fVTswfegQS9YgD2fLnZ1", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(129, 153))\n", + "title": "Reading the list of attached project files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@111317474007.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01KTinBE2LFK2BDqZD8HsTdG", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(174, 194))\n", + "title": "Reading Rachel's rules about modifying files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@111326781527.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01Aq1AHNoywiTg81RXwSjqax", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(132, 152))\n", + "title": "Reading the attached files message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@111337448435.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01ADP3hYZk5ydBFRaQYCe6kQ", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(178, 194))\n", + "title": "Reading Rachel's rules message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@111346940592.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01MZyirYvf2sXe5W1A2YqhwN", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(134, 151))\nconsole.log(\"=== LAST MSG ===\")\nconsole.log(pc.text.read(199, 211))\n", + "title": "Reading the attachments and the final message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_32_20260923@111358053133.png", + "osworld_action": "WAIT" + }, + { + "step_num": 33, + "action": { + "id": "toolu_bdrk_01J4Uzia8vmzdcvztMunN8qc", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(178, 194))\n", + "title": "Reading Rachel's model requirements message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_33_20260923@111407433901.png", + "osworld_action": "WAIT" + }, + { + "step_num": 34, + "action": { + "id": "toolu_bdrk_019wZb2sgzSfbbQSQcnstoAX", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(135, 151))\n", + "title": "Reading the attached file names" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_34_20260923@111416833233.png", + "osworld_action": "WAIT" + }, + { + "step_num": 35, + "action": { + "id": "toolu_bdrk_013rdNuRK4abswgsTj5GMqL2", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(179, 194))\n", + "title": "Reading the model requirements message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_35_20260923@111426893918.png", + "osworld_action": "WAIT" + }, + { + "step_num": 36, + "action": { + "id": "toolu_bdrk_017eQbBmqZgPbDesNRBgkBPv", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(138, 150))\n", + "title": "Reading the attached ZIP and file descriptions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_36_20260923@111436000588.png", + "osworld_action": "WAIT" + }, + { + "step_num": 37, + "action": { + "id": "toolu_bdrk_01A9x9gnmRUrr8ZUBYr4CP4A", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(182, 194))\n", + "title": "Reading the model.py requirements" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_37_20260923@111445396122.png", + "osworld_action": "WAIT" + }, + { + "step_num": 38, + "action": { + "id": "toolu_bdrk_01Xd4KdtAUW4k3LrYGKkZaje", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(141, 150))\nconsole.log(\"=== 203-210 ===\")\nconsole.log(pc.text.read(203, 210))\n", + "title": "Reading remaining attachment details and last message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_38_20260923@111456262120.png", + "osworld_action": "WAIT" + }, + { + "step_num": 39, + "action": { + "id": "toolu_bdrk_01LJubtogL8aRGWt7boaK8iw", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(185, 194))\n", + "title": "Reading the rest of the model requirements" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_39_20260923@111505635183.png", + "osworld_action": "WAIT" + }, + { + "step_num": 40, + "action": { + "id": "toolu_bdrk_011s9ZUN8pNip3i3C1o8zvXS", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(144, 150))\nconsole.log(\"=== 203-209 ===\")\nconsole.log(pc.text.read(203, 209))\n", + "title": "Reading the final attachment note and exception message" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_40_20260923@111515390827.png", + "osworld_action": "WAIT" + }, + { + "step_num": 41, + "action": { + "id": "toolu_bdrk_01LwrSEWLAdMQb4VKVWxThun", + "name": "execute", + "input": { + "code": "console.log(pc.text.read(186, 192))\n", + "title": "Reading Alex's question about modifying run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_41_20260923@111538292335.png", + "osworld_action": "WAIT" + }, + { + "step_num": 42, + "action": { + "id": "toolu_bdrk_015tT5pXrJ6ndL8SrZ59y6or", + "name": "execute", + "input": { + "code": "var convo = {\n msgs: [\n \"Rachel (28m): Hey Alex! Hope you're doing well. Remember the AI-Assisted Healthcare project we discussed last week? I've finished setting up my part!\",\n \"Rachel (27m): I've prepared all the files you'll need. Here they are: [FILE AI-Assisted_Healthcare.zip 2.3 MB]\",\n \"Rachel (26m): The zip contains: 1. run.py - the evaluation script I wrote; 2. heart_train.csv - training data 3. heart_test.csv - testing data\",\n \"Alex (23m): Hey Rachel! Got it, thanks for sending these over\",\n \"Alex (22m): So what model should I use for this? Logistic Regression?\",\n \"Rachel (18m): Yeah let's start with Logistic Regression as the baseline. It's simple and interpretable - good for a healthcare application. I'll check whether we should switch to something else though.\",\n \"Rachel (17m): For the model part - you need to create a model.py file that works with my run.py script. I've already set up the evaluation pipeline, so just make sure your model is compatible with it.\",\n \"Alex (15m): Got it. So I shouldn't modify run.py at all?\",\n \"Rachel (13m): Actually there's one exception! In run.py, there's a Configurable Parameters section at the top. You CAN modify MAX_DEPTH and MAX_SAMPLES there to tune the hyperparameters. But please don't change anything else in that file.\"\n ]\n}\nvar r = await machine.exec({ command: \"curl -s -i 'https://teamchat.web.hku.icu/' -m 20 | head -40\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking the TeamChat server response" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_42_20260923@111555707307.png", + "osworld_action": "WAIT" + }, + { + "step_num": 43, + "action": { + "id": "toolu_bdrk_018gFAAL9Pok4rN2uB7xZVLT", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"for p in api/messages api/dms api/conversations api/channels api/state api/data messages.json data.json; do echo \\\"--- $p\\\"; curl -s -o /dev/null -w '%{http_code}\\\\n' -m 10 \\\"https://teamchat.web.hku.icu/$p\\\"; done\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Probing TeamChat API endpoints" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_43_20260923@111607732080.png", + "osworld_action": "WAIT" + }, + { + "step_num": 44, + "action": { + "id": "toolu_bdrk_013xQ46mwpJ6qRBbX3w2btv2", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"curl -s -m 15 'https://teamchat.web.hku.icu/api/state' -o /tmp/state.json; ls -la /tmp/state.json; head -c 2000 /tmp/state.json\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Fetching the TeamChat state API" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_44_20260923@111619757672.png", + "osworld_action": "WAIT" + }, + { + "step_num": 45, + "action": { + "id": "toolu_bdrk_011T9qJNPraynVN6E24HigzB", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"curl -s -m 20 'https://teamchat.web.hku.icu/assets/index-DlLyki33.js' -o /tmp/tc.js; wc -c /tmp/tc.js; grep -o '\\\"/api/[a-zA-Z0-9_/${}.-]*\\\"' /tmp/tc.js | sort -u | head -60\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Looking for API routes in the TeamChat bundle" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_45_20260923@111633994769.png", + "osworld_action": "WAIT" + }, + { + "step_num": 46, + "action": { + "id": "toolu_bdrk_01BDLaUkkUDsWkEu6fkmknTQ", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"grep -o 'Rachel[^\\\"]\\\\{0,80\\\\}' /tmp/tc.js | head -20; echo '---urls---'; grep -o 'https\\\\?://[a-zA-Z0-9_./?=&:$-]\\\\{0,60\\\\}' /tmp/tc.js | sort -u | head -40; echo '---fetch---'; grep -o 'fetch(\\\\`\\\\?[^)]\\\\{0,80\\\\}' /tmp/tc.js | head -30\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Searching the bundle for message data sources" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_46_20260923@111646769951.png", + "osworld_action": "WAIT" + }, + { + "step_num": 47, + "action": { + "id": "toolu_bdrk_01Lm1BBeADCnjeyccqCY7Jrs", + "name": "execute", + "input": { + "code": "await chrome.screenshot()\n", + "title": "Viewing the conversation and file attachment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_47_20260923@111700796012.png", + "osworld_action": "WAIT" + }, + { + "step_num": 48, + "action": { + "id": "toolu_bdrk_01YNYs82LqF2EnEsA8qjPahX", + "name": "execute", + "input": { + "code": "await machine.click({ x: 886, y: 390 })\nawait wait({ waitTime: 4 })\nvar r = await machine.exec({ command: \"ls -la ~/Downloads/ 2>/dev/null; echo '---'; ls -la ~/Desktop/\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Downloading the project ZIP file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_48_20260923@111721288652.png", + "osworld_action": "WAIT" + }, + { + "step_num": 49, + "action": { + "id": "toolu_bdrk_01UFB1NBdXkmmag4FgVyAfA7", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Downloads && unzip -l AI-Assisted_Healthcare.zip\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Listing the contents of the ZIP archive" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_49_20260923@111733872967.png", + "osworld_action": "WAIT" + }, + { + "step_num": 50, + "action": { + "id": "toolu_bdrk_01YaMkdpkzQh9fTURU37v8wX", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Downloads && rm -rf /tmp/xz && mkdir -p /tmp/xz && unzip -q -o AI-Assisted_Healthcare.zip -d /tmp/xz && mkdir -p ~/Desktop/AI-Assisted_Healthcare && cp -a /tmp/xz/AI-Assisted_Healthcare/. ~/Desktop/AI-Assisted_Healthcare/ && ls -la ~/Desktop/AI-Assisted_Healthcare/\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Extracting project files to Desktop folder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_50_20260923@111751008056.png", + "osworld_action": "WAIT" + }, + { + "step_num": 51, + "action": { + "id": "toolu_bdrk_01J1vFbSZomBdQCwPe1ebK2H", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && echo '===== run.py ====='; cat run.py; echo '===== .env ====='; cat .env\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading run.py and the .env file" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_51_20260923@111803575328.png", + "osworld_action": "WAIT" + }, + { + "step_num": 52, + "action": { + "id": "toolu_bdrk_01L4BajPCfNrgWxcjMtmE8ck", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && echo '--- head train ---'; head -3 heart_train.csv; echo '--- head test ---'; head -3 heart_test.csv; echo '--- wc ---'; wc -l heart_train.csv heart_test.csv; echo '--- lock files ---'; for f in '~$AI-Assisted_Healthcare.pptx' '~$struction_Document.docx'; do echo \\\"## $f\\\"; cat -v \\\"$f\\\" | head -5; echo; done; echo '--- python ---'; python3 -c 'import sklearn, pandas, numpy; print(sklearn.__version__, pandas.__version__, numpy.__version__)'; python3 -c 'import wandb; print(\\\"wandb\\\", wandb.__version__)'\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Inspecting the datasets and Python environment" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_52_20260923@111827944969.png", + "osworld_action": "WAIT" + }, + { + "step_num": 53, + "action": { + "id": "toolu_bdrk_01JemYioUFF6xPmBTvLxqy8u", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && head -3 heart_train.csv; echo '--- test ---'; head -3 heart_test.csv; echo '--- wc ---'; wc -l heart_train.csv heart_test.csv\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Inspecting the training and test datasets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_53_20260923@111838620133.png", + "osworld_action": "WAIT" + }, + { + "step_num": 54, + "action": { + "id": "toolu_bdrk_014SpDZJ6zxgvJdfW3HKhm3X", + "name": "execute", + "input": { + "code": "var runpy = await machine.readFile({ path: \"/home/user/Desktop/AI-Assisted_Healthcare/run.py\" })\nconsole.log(runpy)\n", + "title": "Reading the full run.py evaluation script" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_54_20260923@111852449877.png", + "osworld_action": "WAIT" + }, + { + "step_num": 55, + "action": { + "id": "toolu_bdrk_01UM9qrEoAKi9U4nfykRsNRq", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && python3 -c \\\"import pptx; print('python-pptx ok')\\\" 2>&1 | tail -2; which pip3 pip; python3 --version; pip3 list 2>/dev/null | head -50\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking available Python packages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_55_20260923@111912155901.png", + "osworld_action": "WAIT" + }, + { + "step_num": 56, + "action": { + "id": "toolu_bdrk_019jeQD33s6mnhyUY6c5cUfp", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"pip3 list 2>/dev/null | grep -iE 'pandas|numpy|scikit|scipy|matplotlib|joblib|lxml|openpyxl|Pillow'; echo '--- net ---'; curl -s -o /dev/null -w '%{http_code}\\\\n' -m 15 https://pypi.org/simple/ ; echo '--- libreoffice ---'; which libreoffice soffice\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking for data science libraries and internet access" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_56_20260923@111926699519.png", + "osworld_action": "WAIT" + }, + { + "step_num": 57, + "action": { + "id": "toolu_bdrk_01W4jsdzMAM7YCLEuupiD9ev", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"nohup pip3 install pandas scikit-learn python-pptx wandb joblib > /tmp/pip.log 2>&1 &\\nsleep 5; tail -5 /tmp/pip.log\", timeout: 60 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Installing required Python packages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_57_20260923@111943681353.png", + "osworld_action": "WAIT" + }, + { + "step_num": 58, + "action": { + "id": "toolu_bdrk_01Tv3xCx7gEzxQb8fFZDFHUA", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp && rm -rf pptx && mkdir pptx && cd pptx && unzip -q ~/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx && ls ppt/slides/\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Extracting the PowerPoint file structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_58_20260923@111955606171.png", + "osworld_action": "WAIT" + }, + { + "step_num": 59, + "action": { + "id": "toolu_bdrk_018pyUw4NMyFJ5gGJk7bJrC6", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp/pptx/ppt/slides && for i in 1 2 3 4 5 6 7 8 9 10 11 12; do echo \"=========== SLIDE $i ===========\"; python3 -c \"\nimport re,sys\nd=open('slide$i.xml',encoding='utf-8').read()\n# extract paragraphs\nfor p in re.findall(r'.*?', d, re.S):\n ts=re.findall(r'(.*?)', p, re.S)\n line=''.join(ts)\n if line.strip(): print(' |', line)\n\"; done` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading all slide text from the PowerPoint" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_59_20260923@112007477021.png", + "osworld_action": "WAIT" + }, + { + "step_num": 60, + "action": { + "id": "toolu_bdrk_01DpM2A7qFzkuG7DNjVapT7J", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(0, 1800))\n", + "title": "Reading the first part of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_60_20260923@112029428222.png", + "osworld_action": "WAIT" + }, + { + "step_num": 61, + "action": { + "id": "toolu_bdrk_01HH1qcfjo2oaF8RvdWQ22Y4", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(1780))\n", + "title": "Reading the rest of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_61_20260923@112043619385.png", + "osworld_action": "WAIT" + }, + { + "step_num": 62, + "action": { + "id": "toolu_bdrk_01X86cEahB1Va9BrBiKgU8NK", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(0, 1200))\n", + "title": "Reading the middle section of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_62_20260923@112053013130.png", + "osworld_action": "WAIT" + }, + { + "step_num": 63, + "action": { + "id": "toolu_bdrk_01As73yc2oUnYRRwmxAf2krS", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(1200, 2100))\n", + "title": "Reading the metrics section of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_63_20260923@112102517580.png", + "osworld_action": "WAIT" + }, + { + "step_num": 64, + "action": { + "id": "toolu_bdrk_01FBWQyjrLWTPNhS579qxBrW", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(2100, 3200))\n", + "title": "Reading the main function of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_64_20260923@112113128893.png", + "osworld_action": "WAIT" + }, + { + "step_num": 65, + "action": { + "id": "toolu_bdrk_016Qv4FrQQ78crLeXrz9uX6V", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(240, 1000))\n", + "title": "Reading the wandb setup section of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_65_20260923@112124538721.png", + "osworld_action": "WAIT" + }, + { + "step_num": 66, + "action": { + "id": "toolu_bdrk_01K1oSzAh4399swYKG541KQz", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(2250, 2900))\n", + "title": "Reading the model usage section of run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_66_20260923@112143241435.png", + "osworld_action": "WAIT" + }, + { + "step_num": 67, + "action": { + "id": "toolu_bdrk_014wXJJzz1hQPAohx73JZ6Fz", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"tail -3 /tmp/pip.log; echo '---'; python3 -c \\\"import pandas,sklearn,pptx,wandb;print('ALL OK', pandas.__version__, sklearn.__version__)\\\" 2>&1 | tail -3\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking package installation progress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_67_20260923@112158917993.png", + "osworld_action": "WAIT" + }, + { + "step_num": 68, + "action": { + "id": "toolu_bdrk_018JsyehVArrzfgRwQHQa7uH", + "name": "execute", + "input": { + "code": "var script = `\nfrom pptx import Presentation\nfrom pptx.util import Emu\np = Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor i, s in enumerate(p.slides, 1):\n print(f\"########## SLIDE {i} ##########\")\n for sh in s.shapes:\n print(f\" [shape] name={sh.shape_name if hasattr(sh,'shape_name') else sh.name!r} type={sh.shape_type} has_tf={sh.has_text_frame} has_table={sh.has_table}\")\n if sh.has_text_frame:\n for pi, para in enumerate(sh.text_frame.paragraphs):\n t = \"\".join(r.text for r in para.runs)\n if t.strip(): print(f\" p{pi}: {t!r}\")\n if sh.has_table:\n tb = sh.table\n for ri, row in enumerate(tb.rows):\n cells = [c.text for c in row.cells]\n print(f\" r{ri}: {cells}\")\n`\nawait machine.writeToFile({ text: script, path: \"/tmp/dump.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp && python3 dump.py > /tmp/dump.txt 2>&1; wc -l /tmp/dump.txt; sed -n '1,60p' /tmp/dump.txt\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Dumping the full PowerPoint structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_68_20260923@112213855419.png", + "osworld_action": "WAIT" + }, + { + "step_num": 69, + "action": { + "id": "toolu_bdrk_0158KHmvg5qonC8gZTtaymEp", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"sed -n '60,120p' /tmp/dump.txt\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading the remaining slides structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_69_20260923@112225088318.png", + "osworld_action": "WAIT" + }, + { + "step_num": 70, + "action": { + "id": "toolu_bdrk_014vXwQbpEW5XpYHffzbdpja", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"sed -n '1,60p' /tmp/dump.txt\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading slides 1 through 6 structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_70_20260923@112255795956.png", + "osworld_action": "WAIT" + }, + { + "step_num": 71, + "action": { + "id": "toolu_bdrk_017n8sfLbstoBBD76UmUuR86", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"grep -n 'SLIDE 7' -A 200 /tmp/dump.txt | sed -n '1,70p'\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading slides 7 through 9 in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_71_20260923@112307872954.png", + "osworld_action": "WAIT" + }, + { + "step_num": 72, + "action": { + "id": "toolu_bdrk_014mcFsgPQZVWGcziP2eoC6Y", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"sed -n '/SLIDE 5/,/SLIDE 7/p' /tmp/dump.txt\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading slides 5 and 6 table structures" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_72_20260923@112320257338.png", + "osworld_action": "WAIT" + }, + { + "step_num": 73, + "action": { + "id": "toolu_bdrk_01FPcamAuoCCwrKKJzsDvBAy", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"sed -n '/SLIDE 7/,/SLIDE 12/p' /tmp/dump.txt\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading slides 7 to 11 in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_73_20260923@112330316122.png", + "osworld_action": "WAIT" + }, + { + "step_num": 74, + "action": { + "id": "toolu_bdrk_01Kg6WUsbqvnM213kSu1je3k", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"awk '/SLIDE 5/,/SLIDE 6/' /tmp/dump.txt\" })\nconsole.log(r.stdout)\n", + "title": "Reading slide 5 data analysis table" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_74_20260923@112342886588.png", + "osworld_action": "WAIT" + }, + { + "step_num": 75, + "action": { + "id": "toolu_bdrk_01ALvZCqphsaMki5sDRagc8g", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"awk '/SLIDE 1 /,/SLIDE 5/' /tmp/dump.txt\" })\nconsole.log(r.stdout)\n", + "title": "Reading slides 1 through 4 content" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_75_20260923@112356080626.png", + "osworld_action": "WAIT" + }, + { + "step_num": 76, + "action": { + "id": "toolu_bdrk_017nrrJUBFkCH2TS2SmWXpZc", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"awk '/SLIDE 6/,/SLIDE 7/' /tmp/dump.txt; echo; awk '/SLIDE 8/,/SLIDE 12/' /tmp/dump.txt\" })\nconsole.log(r.stdout)\n", + "title": "Reading slides 6 and 8 through 11 in detail" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_76_20260923@112406913059.png", + "osworld_action": "WAIT" + }, + { + "step_num": 77, + "action": { + "id": "toolu_bdrk_011k35twZ9HQxMnitSqDxjxb", + "name": "execute", + "input": { + "code": "console.log(runpy.slice(2380, 2760))\n", + "title": "Confirming how run.py calls the model" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_77_20260923@112443378261.png", + "osworld_action": "WAIT" + }, + { + "step_num": 78, + "action": { + "id": "toolu_bdrk_013VzpWgg21eAKoE7FoU1qWe", + "name": "execute", + "input": { + "code": "var modelpy = `\"\"\"\nmodel.py\n========\nRandom Forest model for the AI-Assisted Healthcare project\n(Heart Disease Prediction Using Machine Learning).\n\nThe class exposed here, \\`HeartDiseaseModel\\`, is the interface expected by\n\\`run.py\\`:\n\n model = HeartDiseaseModel(n_estimators=..., max_depth=..., max_samples=...,\n random_state=..., n_jobs=...)\n model.train(train_file)\n model.save_model(model_file)\n predictions_df = model.predict(test_file)\n X = model.preprocess_data(X, is_training=False)\n y_pred = model.get_predictions(X)\n\"\"\"\n\nimport pickle\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\n\n\nclass HeartDiseaseModel:\n \"\"\"Random Forest classifier for heart-disease prediction.\"\"\"\n\n TARGET_COLUMN = \"HeartDisease\"\n\n # Categorical features -> integer encoded (categories sorted alphabetically)\n CATEGORICAL_COLUMNS = [\n \"Sex\",\n \"ChestPainType\",\n \"RestingECG\",\n \"ExerciseAngina\",\n \"ST_Slope\",\n ]\n\n # Continuous features -> zero mean / unit variance\n STANDARD_SCALE_COLUMNS = [\"Age\", \"RestingBP\", \"Cholesterol\", \"MaxHR\"]\n\n # Skewed, bounded feature -> squeezed into [0, 1]\n MINMAX_SCALE_COLUMNS = [\"Oldpeak\"]\n\n def __init__(\n self,\n n_estimators=100,\n max_depth=None,\n max_samples=None,\n random_state=42,\n n_jobs=-1,\n ):\n self.n_estimators = n_estimators\n self.max_depth = max_depth\n self.max_samples = max_samples\n self.random_state = random_state\n self.n_jobs = n_jobs\n\n self.model = RandomForestClassifier(\n n_estimators=n_estimators,\n max_depth=max_depth,\n max_samples=max_samples,\n random_state=random_state,\n n_jobs=n_jobs,\n )\n\n self.standard_scaler = StandardScaler()\n self.minmax_scaler = MinMaxScaler()\n self.category_mappings = {}\n self.feature_columns = None\n self.is_fitted = False\n\n # ------------------------------------------------------------------\n # Data preparation\n # ------------------------------------------------------------------\n def _encode_categorical(self, X, is_training):\n for column in self.CATEGORICAL_COLUMNS:\n if column not in X.columns:\n continue\n if is_training:\n categories = sorted(X[column].dropna().astype(str).unique())\n self.category_mappings[column] = {\n category: index for index, category in enumerate(categories)\n }\n mapping = self.category_mappings.get(column, {})\n X[column] = X[column].astype(str).map(mapping)\n # Unseen / missing categories fall back to -1\n X[column] = X[column].fillna(-1).astype(int)\n return X\n\n def _scale_numeric(self, X, is_training):\n std_columns = [c for c in self.STANDARD_SCALE_COLUMNS if c in X.columns]\n mm_columns = [c for c in self.MINMAX_SCALE_COLUMNS if c in X.columns]\n\n if std_columns:\n if is_training:\n X[std_columns] = self.standard_scaler.fit_transform(X[std_columns])\n else:\n X[std_columns] = self.standard_scaler.transform(X[std_columns])\n\n if mm_columns:\n if is_training:\n X[mm_columns] = self.minmax_scaler.fit_transform(X[mm_columns])\n else:\n X[mm_columns] = self.minmax_scaler.transform(X[mm_columns])\n\n return X\n\n def preprocess_data(self, X, is_training=False):\n \"\"\"Encode categorical features and scale numeric ones.\n\n Parameters\n ----------\n X : pandas.DataFrame\n Feature frame (the target column is dropped if still present).\n is_training : bool\n When True the encoders/scalers are fitted on \\`X\\`; otherwise the\n statistics learned during training are re-used.\n \"\"\"\n X = pd.DataFrame(X).copy()\n\n if self.TARGET_COLUMN in X.columns:\n X = X.drop(columns=[self.TARGET_COLUMN])\n\n X = self._encode_categorical(X, is_training)\n\n # Numeric columns: fill any gaps before scaling\n numeric_columns = [c for c in X.columns if c not in self.CATEGORICAL_COLUMNS]\n for column in numeric_columns:\n X[column] = pd.to_numeric(X[column], errors=\"coerce\")\n if is_training:\n self.numeric_medians = X[numeric_columns].median()\n medians = getattr(self, \"numeric_medians\", None)\n if medians is not None:\n X[numeric_columns] = X[numeric_columns].fillna(medians)\n X[numeric_columns] = X[numeric_columns].fillna(0)\n\n X = self._scale_numeric(X, is_training)\n\n if is_training:\n self.feature_columns = list(X.columns)\n elif self.feature_columns is not None:\n for column in self.feature_columns:\n if column not in X.columns:\n X[column] = 0\n X = X[self.feature_columns]\n\n return X\n\n # ------------------------------------------------------------------\n # Training / inference\n # ------------------------------------------------------------------\n def train(self, train_file):\n \"\"\"Fit the Random Forest on the training CSV.\"\"\"\n df = pd.read_csv(train_file)\n\n if self.TARGET_COLUMN not in df.columns:\n raise ValueError(\n \"Training data must contain the '%s' column\" % self.TARGET_COLUMN\n )\n\n y = df[self.TARGET_COLUMN].astype(int)\n X = df.drop(columns=[self.TARGET_COLUMN])\n\n X = self.preprocess_data(X, is_training=True)\n self.model.fit(X, y)\n self.is_fitted = True\n\n train_accuracy = float((self.model.predict(X) == y).mean())\n print(\n \"Model trained on %d samples with %d features \"\n \"(max_depth=%s, max_samples=%s)\"\n % (X.shape[0], X.shape[1], self.max_depth, self.max_samples)\n )\n print(\"Training accuracy: %.4f\" % train_accuracy)\n return self\n\n def get_predictions(self, X):\n \"\"\"Return class predictions for already preprocessed features.\"\"\"\n if not self.is_fitted:\n raise RuntimeError(\"Model must be trained before predicting\")\n return self.model.predict(X)\n\n def get_probabilities(self, X):\n \"\"\"Return probability of the positive class for preprocessed features.\"\"\"\n if not self.is_fitted:\n raise RuntimeError(\"Model must be trained before predicting\")\n return self.model.predict_proba(X)[:, 1]\n\n def predict(self, test_file):\n \"\"\"Predict on a CSV file and return a DataFrame with the results.\"\"\"\n df = pd.read_csv(test_file)\n\n X_raw = df.drop(columns=[self.TARGET_COLUMN], errors=\"ignore\")\n X = self.preprocess_data(X_raw, is_training=False)\n\n y_pred = self.get_predictions(X)\n y_prob = self.get_probabilities(X)\n\n predictions_df = pd.DataFrame(\n {\n \"PredictedHeartDisease\": y_pred.astype(int),\n \"Probability\": np.round(y_prob, 4),\n }\n )\n if self.TARGET_COLUMN in df.columns:\n predictions_df.insert(0, \"HeartDisease\", df[self.TARGET_COLUMN].values)\n\n print(\"Generated %d predictions\" % len(predictions_df))\n return predictions_df\n\n # ------------------------------------------------------------------\n # Persistence\n # ------------------------------------------------------------------\n def save_model(self, model_file):\n \"\"\"Persist the fitted model (and its preprocessing state) to disk.\"\"\"\n with open(model_file, \"wb\") as handle:\n pickle.dump(self, handle)\n print(\"Model saved to %s\" % model_file)\n return model_file\n\n @staticmethod\n def load_model(model_file):\n \"\"\"Load a model previously stored with \\`save_model\\`.\"\"\"\n with open(model_file, \"rb\") as handle:\n return pickle.load(handle)\n`\nawait machine.writeToFile({ text: modelpy, path: \"/home/user/Desktop/AI-Assisted_Healthcare/model.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && python3 -c \\\"import ast;ast.parse(open('model.py').read());print('syntax ok')\\\" && wc -l model.py\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Writing the Random Forest model implementation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_78_20260923@112538414433.png", + "osworld_action": "WAIT" + }, + { + "step_num": 79, + "action": { + "id": "toolu_bdrk_012bbk9mAA8Faqw1pHJRqVXY", + "name": "execute", + "input": { + "code": "var analysis = `\nimport json, random\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\nfrom model import HeartDiseaseModel\n\nnp.random.seed(42); random.seed(42)\n\ntrain = pd.read_csv('heart_train.csv')\ntest = pd.read_csv('heart_test.csv')\nout = {}\n\nnum_cols = ['Age','RestingBP','Cholesterol','FastingBS','MaxHR','Oldpeak','HeartDisease']\ndesc = {}\nfor src_name, df in [('train', train), ('test', test), ('all', pd.concat([train,test], ignore_index=True))]:\n d = df[num_cols].describe()\n desc[src_name] = {m: {c: round(float(d.loc[m, c]), 2) for c in num_cols} for m in ['count','mean','std','min','max']}\nout['describe'] = desc\n\n# ---- scaled first five rows (training data) ----\nm = HeartDiseaseModel(n_estimators=100, max_depth=6, max_samples=0.1, random_state=42, n_jobs=-1)\nXtr = train.drop(columns=['HeartDisease'])\nXs = m.preprocess_data(Xtr, is_training=True)\nscaled = Xs.copy()\nscaled['HeartDisease'] = train['HeartDisease'].values\ncols = ['Age','Sex','ChestPainType','RestingBP','Cholesterol','FastingBS','RestingECG','MaxHR','ExerciseAngina','Oldpeak','ST_Slope','HeartDisease']\nscaled = scaled[cols]\nout['scaled_head_train'] = [[round(float(v),2) for v in row] for row in scaled.head(5).values]\nout['scaled_cols'] = cols\nout['cat_maps'] = {k: {str(a): int(b) for a,b in v.items()} for k,v in m.category_mappings.items()}\n\n# raw first five rows for reference\nout['raw_head_train'] = train[cols].head(5).astype(str).values.tolist()\n\n# ---- grid search ----\ndepths = [4,5,6,7,8,9,10]\nsamples = [0.1,0.3,0.5,0.7,0.9]\ngrid = {}\nbest = None\ny_true = test['HeartDisease']\nfor d in depths:\n for s in samples:\n np.random.seed(42); random.seed(42)\n mm = HeartDiseaseModel(n_estimators=100, max_depth=d, max_samples=s, random_state=42, n_jobs=-1)\n mm.train('heart_train.csv')\n Xt = mm.preprocess_data(test.drop(columns=['HeartDisease']), is_training=False)\n yp = mm.get_predictions(Xt)\n acc = accuracy_score(y_true, yp)\n grid[f\"{d}|{s}\"] = round(float(acc), 4)\n met = dict(accuracy=float(acc),\n precision=float(precision_score(y_true, yp, zero_division=0)),\n recall=float(recall_score(y_true, yp, zero_division=0)),\n f1=float(f1_score(y_true, yp, zero_division=0)))\n if best is None or met['accuracy'] > best['acc_raw']:\n pass\n grid.setdefault('_metrics', {})[f\"{d}|{s}\"] = {k: round(v,4) for k,v in met.items()}\nout['grid'] = grid\njson.dump(out, open('/tmp/analysis.json','w'), indent=1)\nprint(\"DONE\")\n`\nawait machine.writeToFile({ text: analysis, path: \"/home/user/Desktop/AI-Assisted_Healthcare/_analysis.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && nohup python3 _analysis.py > /tmp/analysis.log 2>&1 & sleep 3; tail -5 /tmp/analysis.log\", timeout: 60 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Running data analysis and hyperparameter grid search" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_79_20260923@112606236287.png", + "osworld_action": "WAIT" + }, + { + "step_num": 80, + "action": { + "id": "toolu_bdrk_01UAHY3eoHRKMYsSov9jzABE", + "name": "execute", + "input": { + "code": "await chrome.activate()\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Checking TeamChat for new messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_80_20260923@112620585577.png", + "osworld_action": "WAIT" + }, + { + "step_num": 81, + "action": { + "id": "toolu_bdrk_019x8zbKsFdPuWX1qQ52sPAa", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"tail -3 /tmp/analysis.log; echo '--- json ---'; ls -la /tmp/analysis.json 2>/dev/null\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking grid search progress" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_81_20260923@112636758274.png", + "osworld_action": "WAIT" + }, + { + "step_num": 82, + "action": { + "id": "toolu_bdrk_01RWjuXFRKrRJTsziVH49fcX", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `python3 -c \"\nimport json\nd=json.load(open('/tmp/analysis.json'))\nprint('CAT MAPS:', json.dumps(d['cat_maps']))\nprint()\nprint('DESCRIBE TRAIN:')\nfor m,v in d['describe']['train'].items(): print(' ',m, v)\nprint()\nprint('DESCRIBE ALL:')\nfor m,v in d['describe']['all'].items(): print(' ',m, v)\n\"` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reviewing dataset statistics" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_82_20260923@112648688639.png", + "osworld_action": "WAIT" + }, + { + "step_num": 83, + "action": { + "id": "toolu_bdrk_01S1Apmcyamg9j6FGoHnV7g6", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `python3 -c \"\nimport json\nd=json.load(open('/tmp/analysis.json'))\ng=d['grid']\ndepths=[4,5,6,7,8,9,10]; samples=[0.1,0.3,0.5,0.7,0.9]\nprint('rows=max_depth(int), cols=max_samples(frac)')\nprint(' ' + ' '.join(f'{s:>6}' for s in samples))\nfor dd in depths:\n print(f'{dd:>4} ' + ' '.join(f\\\\\\\"{g[f'{dd}|{s}']:.4f}\\\\\\\" for s in samples))\nprint()\nbest=max(g[f'{dd}|{s}'] for dd in depths for s in samples)\nprint('best acc', best)\ncands=[(dd,s) for dd in depths for s in samples if g[f'{dd}|{s}']==best]\nprint('candidates (depth,samples):', cands)\nprint()\nprint('metrics for each candidate:')\nfor dd,s in cands: print(' ', dd, s, g['_metrics'][f'{dd}|{s}'])\n\"` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reviewing the hyperparameter grid search results" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_83_20260923@112702106108.png", + "osworld_action": "WAIT" + }, + { + "step_num": 84, + "action": { + "id": "toolu_bdrk_01FU5KMiBwvBGisJ7U2dbCzj", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"awk '/SLIDE 8/,/SLIDE 9/' /tmp/dump.txt\" })\nconsole.log(r.stdout)\n", + "title": "Re-reading slide 8 results text exactly" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_84_20260923@112724852155.png", + "osworld_action": "WAIT" + }, + { + "step_num": 85, + "action": { + "id": "toolu_bdrk_01ADZrp15ooZhcXgPhwG2Qem", + "name": "execute", + "input": { + "code": "var script2 = `\nfrom pptx import Presentation\nfrom pptx.util import Emu\np = Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor idx in [8,9,10,11]:\n s = p.slides[idx-1]\n print(f\"##### SLIDE {idx} (w={p.slide_width}, h={p.slide_height})\")\n for sh in s.shapes:\n print(f\" {sh.name!r} L={Emu(sh.left).inches:.2f} T={Emu(sh.top).inches:.2f} W={Emu(sh.width).inches:.2f} H={Emu(sh.height).inches:.2f} table={sh.has_table}\")\n if sh.has_text_frame and sh.text_frame.text.strip():\n print(\" text:\", repr(sh.text_frame.text)[:200])\n if sh.has_table:\n for ri,row in enumerate(sh.table.rows):\n print(\" r%d:\"%ri, [c.text for c in row.cells])\n`\nawait machine.writeToFile({ text: script2, path: \"/tmp/dump2.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp && python3 dump2.py 2>&1 | grep -v NumPy | grep -v warnings.warn\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking slide layouts and table axis labels" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_85_20260923@112757622191.png", + "osworld_action": "WAIT" + }, + { + "step_num": 86, + "action": { + "id": "toolu_bdrk_01XqmkqFNhfRCvSXuSYRHY5Y", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `python3 -c \"\nimport json\nd=json.load(open('/tmp/analysis.json'))\ng=d['grid']\ndepths=[4,5,6,7,8,9,10]; samples=[0.1,0.3,0.5,0.7,0.9]\nprint(json.dumps({str(dd): [g[f'{dd}|{s}'] for s in samples] for dd in depths}))\nprint()\nprint('SCALED HEAD:', json.dumps(d['scaled_head_train']))\nprint()\nprint('COLS:', json.dumps(d['scaled_cols']))\nprint()\nprint('CATMAP:', json.dumps(d['cat_maps']))\n\"` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Extracting the accuracy grid and scaled data" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_86_20260923@112826913998.png", + "osworld_action": "WAIT" + }, + { + "step_num": 87, + "action": { + "id": "toolu_bdrk_01Xf8zscBnkvAv371xdPC2xx", + "name": "execute", + "input": { + "code": "var results = {\n depths: [4,5,6,7,8,9,10],\n samples: [0.1,0.3,0.5,0.7,0.9],\n grid: {\"4\":[0.8804,0.8696,0.8750,0.8750,0.8804],\"5\":[0.8859,0.8750,0.8804,0.8641,0.8750],\"6\":[0.8967,0.8913,0.8913,0.8804,0.8967],\"7\":[0.8913,0.8804,0.8913,0.8913,0.8859],\"8\":[0.9022,0.8967,0.8913,0.8967,0.8859],\"9\":[0.8913,0.8859,0.8967,0.9022,0.8804],\"10\":[0.8967,0.8859,0.9022,0.8859,0.8967]},\n bestAcc: 0.9022,\n bestIntParam: 8, // integer axis (sklearn max_depth; PPT calls it max_samples)\n bestFracParam: 0.1, // fraction axis (sklearn max_samples; PPT calls it max_depth)\n bestMetrics: { accuracy: 0.9022, precision: 0.8889, recall: 0.9412, f1: 0.9143 },\n scaledHead: [[0.97,1.0,1.0,0.34,0.13,1.0,2.0,-0.32,1.0,0.46,1.0,1.0],[0.12,1.0,1.0,-1.27,0.10,1.0,2.0,1.69,0.0,0.37,2.0,0.0],[0.55,0.0,0.0,-0.16,1.24,1.0,2.0,-0.25,1.0,0.50,1.0,1.0],[-0.73,1.0,0.0,1.50,-1.88,0.0,1.0,-0.48,1.0,0.32,1.0,1.0],[-0.41,1.0,0.0,0.67,-1.88,1.0,1.0,0.10,1.0,0.40,1.0,1.0]],\n catMap: {\"Sex\":{\"F\":0,\"M\":1},\"ChestPainType\":{\"ASY\":0,\"ATA\":1,\"NAP\":2,\"TA\":3},\"RestingECG\":{\"LVH\":0,\"Normal\":1,\"ST\":2},\"ExerciseAngina\":{\"N\":0,\"Y\":1},\"ST_Slope\":{\"Down\":0,\"Flat\":1,\"Up\":2}}\n}\nvar r = await machine.exec({ command: \"cd /tmp && python3 - <<'EOF' 2>&1 | grep -v NumPy | grep -v warnings.warn\\nfrom pptx import Presentation\\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\\nfor idx in [5,6,7,9,10]:\\n print('##### SLIDE', idx)\\n for sh in p.slides[idx-1].shapes:\\n if sh.has_text_frame and sh.text_frame.text.strip():\\n print(' ', sh.name, '::', repr(sh.text_frame.text))\\nEOF\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading the exact text of slides 5, 6, 7, 9 and 10" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_87_20260923@112900435874.png", + "osworld_action": "WAIT" + }, + { + "step_num": 88, + "action": { + "id": "toolu_bdrk_015iNPrjJC85DWUmPuKWCauZ", + "name": "execute", + "input": { + "code": "await chrome.activate()\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Checking TeamChat for Rachel's follow-up" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_88_20260923@112928376807.png", + "osworld_action": "WAIT" + }, + { + "step_num": 89, + "action": { + "id": "toolu_bdrk_01NxRNCCj1N5abkwvGuTs7LX", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<8;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Scrolling to the latest messages in the conversation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_89_20260923@112947885405.png", + "osworld_action": "WAIT" + }, + { + "step_num": 90, + "action": { + "id": "toolu_bdrk_01BUvWna8EtxHFz3Jf1TaiXz", + "name": "execute", + "input": { + "code": "await machine.click({ x: 233, y: 649 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Checking Jason's messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_90_20260923@113007242155.png", + "osworld_action": "WAIT" + }, + { + "step_num": 91, + "action": { + "id": "toolu_bdrk_017h6PcxKGW7ruTEvhPJDbx9", + "name": "execute", + "input": { + "code": "await machine.click({ x: 233, y: 683 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Checking Emily's messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_91_20260923@113021910204.png", + "osworld_action": "WAIT" + }, + { + "step_num": 92, + "action": { + "id": "toolu_bdrk_017vUWezH725Q9icjkvBhqib", + "name": "execute", + "input": { + "code": "await machine.click({ x: 233, y: 717 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Checking David's messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_92_20260923@113038671618.png", + "osworld_action": "WAIT" + }, + { + "step_num": 93, + "action": { + "id": "toolu_bdrk_01UQgdhXsu17YzfQwNvLRNAM", + "name": "execute", + "input": { + "code": "await machine.click({ x: 233, y: 615 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<10;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Reading Rachel's newest messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_93_20260923@113058008336.png", + "osworld_action": "WAIT" + }, + { + "step_num": 94, + "action": { + "id": "toolu_bdrk_01RtgqGP9R3rdYdB7VueBoPh", + "name": "execute", + "input": { + "code": "await machine.click({ x: 255, y: 505 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Checking the course channel for assignment details" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_94_20260923@113118092293.png", + "osworld_action": "WAIT" + }, + { + "step_num": 95, + "action": { + "id": "toolu_bdrk_01Ehvh5kw8DkGSvaDvpjdfL8", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp && python3 - <<'EOF' 2>&1 | grep -v NumPy | grep -v warnings.warn\\nfrom pptx import Presentation\\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\\nfor idx in [1,2,3,4]:\\n print('##### SLIDE', idx)\\n for sh in p.slides[idx-1].shapes:\\n if sh.has_text_frame and sh.text_frame.text.strip():\\n print(' ', sh.name, '::', sh.text_frame.text)\\nEOF\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Reading the dataset overview slides" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_95_20260923@113145591336.png", + "osworld_action": "WAIT" + }, + { + "step_num": 96, + "action": { + "id": "toolu_bdrk_017JQYMQrtuPbGELacgWLync", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd ~/Desktop/AI-Assisted_Healthcare && python3 - <<'EOF' 2>&1 | grep -v NumPy | grep -v warnings.warn\nimport pandas as pd, json\npd.set_option('display.width', 250)\ntrain = pd.read_csv('heart_train.csv')\nnum = ['Age','RestingBP','Cholesterol','FastingBS','MaxHR','Oldpeak','HeartDisease']\nd = train[num].describe().loc[['count','mean','std','min','max']]\nprint(d.round(2).to_string())\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Computing descriptive statistics for slide 5" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_96_20260923@113203373327.png", + "osworld_action": "WAIT" + }, + { + "step_num": 97, + "action": { + "id": "toolu_bdrk_01EAd18ioa3o9qtG9aHrZZS3", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd ~/Desktop/AI-Assisted_Healthcare && python3 - <<'EOF' 2>&1 | grep -v NumPy | grep -v warnings.warn\nimport pandas as pd, json\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\npd.set_option('display.width', 300)\ntrain = pd.read_csv('heart_train.csv')\ncols = ['Age','Sex','ChestPainType','RestingBP','Cholesterol','FastingBS','RestingECG','MaxHR','ExerciseAngina','Oldpeak','ST_Slope','HeartDisease']\ndf = train[cols].copy()\nstd_cols = ['Age','RestingBP','Cholesterol','MaxHR']\ndf[std_cols] = StandardScaler().fit_transform(train[std_cols])\ndf[['Oldpeak']] = MinMaxScaler().fit_transform(train[['Oldpeak']])\nout = df.head(5).copy()\nfor c in std_cols + ['Oldpeak']:\n out[c] = out[c].map(lambda v: f\"{v:.2f}\")\nprint(out.to_string(index=False))\nprint()\nprint(\"RAW first 5:\")\nprint(train[cols].head(5).to_string(index=False))\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Computing scaled data for slide 6" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_97_20260923@113221073384.png", + "osworld_action": "WAIT" + }, + { + "step_num": 98, + "action": { + "id": "toolu_bdrk_01XAhaN6YMFNt9fDiMoZhWTn", + "name": "execute", + "input": { + "code": "results.slide5 = {\n cols: ['Age','RestingBP','Cholesterol','FastingBS','MaxHR','Oldpeak','HeartDisease'],\n count: [734.00,734.00,734.00,734.00,734.00,734.00,734.00],\n mean: [53.85,132.87,203.23,0.23,136.38,0.87,0.55],\n std: [9.44,18.08,108.40,0.42,25.83,1.06,0.50],\n min: [29.00,92.00,0.00,0.00,60.00,-2.60,0.00],\n max: [77.00,200.00,603.00,1.00,202.00,5.60,1.00]\n}\nresults.slide6 = {\n cols: ['Age','Sex','ChestPainType','RestingBP','Cholesterol','FastingBS','RestingECG','MaxHR','ExerciseAngina','Oldpeak','ST_Slope','HeartDisease'],\n rows: [\n ['0.97','M','ATA','0.34','0.13','1','ST','-0.32','Y','0.46','Flat','1'],\n ['0.12','M','ATA','-1.27','0.10','1','ST','1.69','N','0.37','Up','0'],\n ['0.55','F','ASY','-0.16','1.24','1','ST','-0.25','Y','0.50','Flat','1'],\n ['-0.73','M','ASY','1.50','-1.88','0','Normal','-0.48','Y','0.32','Flat','1'],\n ['-0.41','M','ASY','0.67','-1.88','1','Normal','0.10','Y','0.40','Flat','1']\n ]\n}\n// Update run.py: only the two configurable hyperparameters\nvar r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && cp run.py /tmp/run.py.orig && sed -i 's/^ MAX_DEPTH = 6$/ MAX_DEPTH = 8/; s/^ MAX_SAMPLES = 0.1$/ MAX_SAMPLES = 0.1/' run.py && grep -n -A4 'Configurable Parameters' run.py && diff /tmp/run.py.orig run.py\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Setting the optimal hyperparameters in run.py" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_98_20260923@113242785958.png", + "osworld_action": "WAIT" + }, + { + "step_num": 99, + "action": { + "id": "toolu_bdrk_01Cbf8yCLqt6PGrphw1ZxhjE", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && timeout 180 env WANDB_MODE=offline WANDB_SILENT=true python3 run.py 2>&1 | grep -v NumPy | grep -v 'warnings.warn' | tail -40\", timeout: 200 })\nconsole.log(\"exit:\", r.exitCode)\nconsole.log(r.stdout, r.stderr)\n", + "title": "Running the evaluation pipeline" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_99_20260923@113257857722.png", + "osworld_action": "WAIT" + }, + { + "step_num": 100, + "action": { + "id": "toolu_bdrk_01QrMczY15XzF25tviLcgP3U", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && rm -f _analysis.py && timeout 90 python3 run.py 2>&1 | grep -viE 'numpy|warnings.warn' | tail -25; echo \\\"EXIT=$?\\\"\", timeout: 120 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Verifying the pipeline runs with default settings" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_100_20260923@113324226370.png", + "osworld_action": "WAIT" + }, + { + "step_num": 101, + "action": { + "id": "toolu_bdrk_017Z4WS6JjdXognbkQmUTH72", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"mkdir -p ~/.config/wandb && printf '[default]\\\\nmode = offline\\\\n' > ~/.config/wandb/settings && cd ~/Desktop/AI-Assisted_Healthcare && timeout 120 python3 run.py 2>&1 | grep -viE 'numpy|warnings.warn' | tail -15\", timeout: 150 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Configuring wandb offline mode and re-running" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_101_20260923@113345241773.png", + "osworld_action": "WAIT" + }, + { + "step_num": 102, + "action": { + "id": "toolu_bdrk_01JcZoZtuLFVfHvFxnXNQBwf", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor idx in [5,6,9,11]:\n print('##### SLIDE', idx)\n for sh in p.slides[idx-1].shapes:\n if sh.has_table:\n t=sh.table\n print(' table', sh.name, 'rows=',len(t.rows),'cols=',len(t.columns))\n for ri,row in enumerate(t.rows):\n print(' r%d:'%ri, [c.text for c in row.cells])\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Confirming table structures to fill" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_102_20260923@113403539881.png", + "osworld_action": "WAIT" + }, + { + "step_num": 103, + "action": { + "id": "toolu_bdrk_01W4UCxTjCNzuLL1wAdEqyZz", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor idx in [8,9,10]:\n print('##### SLIDE', idx)\n for sh in p.slides[idx-1].shapes:\n if sh.has_text_frame and sh.text_frame.text.strip():\n print(' SHAPE', sh.name)\n for pi,para in enumerate(sh.text_frame.paragraphs):\n print(' p%d runs='%pi, [r.text for r in para.runs])\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Inspecting slide 8 placeholder text runs" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_103_20260923@113419289839.png", + "osworld_action": "WAIT" + }, + { + "step_num": 104, + "action": { + "id": "toolu_bdrk_017Jpx1C96Ft7ZEibsep6Z9z", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor sh in p.slides[8].shapes:\n print(sh.name, sh.has_table)\n if sh.has_table:\n t=sh.table\n print(' rows',len(t.rows),'cols',len(t.columns))\n for ri,row in enumerate(t.rows): print(' r%d'%ri, [c.text for c in row.cells])\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Checking slide 9 metrics table structure" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_104_20260923@113459638141.png", + "osworld_action": "WAIT" + }, + { + "step_num": 105, + "action": { + "id": "toolu_bdrk_015dKjCvMtxu6mBkyLvMjHp4", + "name": "execute", + "input": { + "code": "var fill = `\nimport copy\nfrom pptx import Presentation\nfrom pptx.util import Pt\n\nSRC = '/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx'\nprs = Presentation(SRC)\n\n\ndef set_cell(cell, text, template_cell):\n \"\"\"Write text into a table cell, inheriting the look of template_cell.\"\"\"\n tf = cell.text_frame\n ttf = template_cell.text_frame\n tpara = ttf.paragraphs[0]\n # rebuild the cell paragraph from the template paragraph (keeps font, align)\n txBody = tf._txBody\n for p in txBody.findall('{http://schemas.openxmlformats.org/drawingml/2006/main}p'):\n txBody.remove(p)\n newp = copy.deepcopy(tpara._p)\n txBody.append(newp)\n para = tf.paragraphs[0]\n # remove all runs but the first, then set its text\n runs = para.runs\n if not runs:\n r = para.add_run()\n runs = para.runs\n for extra in runs[1:]:\n extra._r.getparent().remove(extra._r)\n para.runs[0].text = text\n\n\n# ----------------------------------------------------------------- slide 5\ns5 = {\n 'count': ['734.00', '734.00', '734.00', '734.00', '734.00', '734.00', '734.00'],\n 'mean': ['53.85', '132.87', '203.23', '0.23', '136.38', '0.87', '0.55'],\n 'std': ['9.44', '18.08', '108.40', '0.42', '25.83', '1.06', '0.50'],\n 'min': ['29.00', '92.00', '0.00', '0.00', '60.00', '-2.60', '0.00'],\n 'max': ['77.00', '200.00', '603.00', '1.00', '202.00', '5.60', '1.00'],\n}\ntbl = [sh for sh in prs.slides[4].shapes if sh.has_table][0].table\nfor ri in range(1, len(tbl.rows)):\n label = tbl.cell(ri, 0).text.strip()\n vals = s5[label]\n for ci, v in enumerate(vals, start=1):\n set_cell(tbl.cell(ri, ci), v, tbl.cell(ri, 0))\n\n# ----------------------------------------------------------------- slide 6\ns6 = [\n ['0.97', 'M', 'ATA', '0.34', '0.13', '1', 'ST', '-0.32', 'Y', '0.46', 'Flat', '1'],\n ['0.12', 'M', 'ATA', '-1.27', '0.10', '1', 'ST', '1.69', 'N', '0.37', 'Up', '0'],\n ['0.55', 'F', 'ASY', '-0.16', '1.24', '1', 'ST', '-0.25', 'Y', '0.50', 'Flat', '1'],\n ['-0.73', 'M', 'ASY', '1.50', '-1.88', '0', 'Normal', '-0.48', 'Y', '0.32', 'Flat', '1'],\n ['-0.41', 'M', 'ASY', '0.67', '-1.88', '1', 'Normal', '0.10', 'Y', '0.40', 'Flat', '1'],\n]\ntbl6 = [sh for sh in prs.slides[5].shapes if sh.has_table][0].table\nfor ri, row in enumerate(s6, start=1):\n for ci, v in enumerate(row):\n set_cell(tbl6.cell(ri, ci), v, tbl6.cell(0, ci))\n\n# ----------------------------------------------------------------- slide 8 text\nBEST_INT = '8' # the 4..10 axis (PPT calls it max_samples)\nBEST_FRAC = '0.1' # the 0.1..0.9 axis (PPT calls it max_depth)\nfor sh in prs.slides[7].shapes:\n if not sh.has_text_frame:\n continue\n for para in sh.text_frame.paragraphs:\n runs = para.runs\n joined = ''.join(r.text for r in runs)\n if 'highest accuracy was achieved' not in joined:\n continue\n # first [value] -> max_samples ; second [value] -> max_depth\n seen = 0\n buf = ''\n for r in runs:\n buf += r.text\n # replace run by run, tracking the placeholder occurrences\n pending = ''\n for r in runs:\n t = r.text\n while '[value]' in t:\n seen += 1\n t = t.replace('[value]', BEST_INT if seen == 1 else BEST_FRAC, 1)\n r.text = t\n # handle the case where \"[value]\" is split across runs: \"[\" + \"value]\"\n for idx, r in enumerate(runs):\n if r.text.endswith('[') and idx + 1 < len(runs) and runs[idx + 1].text.startswith('value]'):\n seen += 1\n r.text = r.text[:-1]\n runs[idx + 1].text = runs[idx + 1].text.replace(\n 'value]', BEST_INT if seen == 1 else BEST_FRAC, 1)\n print('slide8 placeholders filled:', seen)\n print('slide8 now:', ''.join(r.text for r in para.runs))\n\n# ----------------------------------------------------------------- slide 9\nmetrics = {'Accuracy': '0.9022', 'Precision': '0.8889', 'Recall': '0.9412', 'F1-Score': '0.9143'}\ntbl9 = [sh for sh in prs.slides[8].shapes if sh.has_table][0].table\nfor ri in range(len(tbl9.rows)):\n name = tbl9.cell(ri, 0).text.strip()\n set_cell(tbl9.cell(ri, 1), metrics[name], tbl9.cell(ri, 0))\n\n# ----------------------------------------------------------------- slide 11 grid\ngrid = {\n 4: ['0.8804', '0.8696', '0.8750', '0.8750', '0.8804'],\n 5: ['0.8859', '0.8750', '0.8804', '0.8641', '0.8750'],\n 6: ['0.8967', '0.8913', '0.8913', '0.8804', '0.8967'],\n 7: ['0.8913', '0.8804', '0.8913', '0.8913', '0.8859'],\n 8: ['0.9022', '0.8967', '0.8913', '0.8967', '0.8859'],\n 9: ['0.8913', '0.8859', '0.8967', '0.9022', '0.8804'],\n 10: ['0.8967', '0.8859', '0.9022', '0.8859', '0.8967'],\n}\ntbl11 = [sh for sh in prs.slides[10].shapes if sh.has_table][0].table\nfor ri in range(1, len(tbl11.rows)):\n key = int(tbl11.cell(ri, 0).text.strip())\n for ci, v in enumerate(grid[key], start=1):\n set_cell(tbl11.cell(ri, ci), v, tbl11.cell(ri, 0))\n\nprs.save(SRC)\nprint('SAVED', SRC)\n`\nawait machine.writeToFile({ text: fill, path: \"/tmp/fill.py\", overwrite: true })\nvar r = await machine.exec({ command: \"cd /tmp && python3 fill.py 2>&1 | grep -viE 'numpy|warnings.warn'\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Filling in the PowerPoint results tables" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_105_20260923@113542299212.png", + "osworld_action": "WAIT" + }, + { + "step_num": 106, + "action": { + "id": "toolu_bdrk_015fdDz8H4AbETYqJHQRk6CS", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx')\nfor idx in [5,6,9,11]:\n print('##### SLIDE', idx)\n for sh in p.slides[idx-1].shapes:\n if sh.has_table:\n for ri,row in enumerate(sh.table.rows):\n print(' ', [c.text for c in row.cells])\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Verifying the filled PowerPoint tables" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_106_20260923@113554871906.png", + "osworld_action": "WAIT" + }, + { + "step_num": 107, + "action": { + "id": "toolu_bdrk_01LoHkCFibCsjxJcEZ5mhpd5", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp && rm -rf pngout && mkdir pngout && timeout 180 soffice --headless --convert-to pdf --outdir /tmp/pngout ~/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx >/dev/null 2>&1; ls -la /tmp/pngout; which pdftoppm\", timeout: 200 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Converting the presentation for visual verification" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_107_20260923@113610212500.png", + "osworld_action": "WAIT" + }, + { + "step_num": 108, + "action": { + "id": "toolu_bdrk_01EnNbBYbiScpSrPJozZhiXG", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd /tmp/pngout && pdftoppm -png -r 60 -f 5 -l 6 AI-Assisted_Healthcare.pdf s && pdftoppm -png -r 60 -f 11 -l 11 AI-Assisted_Healthcare.pdf t && mkdir -p ~/SimularFiles/tmp 2>/dev/null; ls\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Rendering slides to images for inspection" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_108_20260923@113621568762.png", + "osworld_action": "WAIT" + }, + { + "step_num": 109, + "action": { + "id": "toolu_bdrk_01XcudPwZrzxvsYBBvHTkdYo", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cp /tmp/pngout/s-05.png /tmp/pngout/s-06.png /tmp/pngout/t-11.png /root/SimularFiles/ 2>/dev/null || (mkdir -p ~/SimularFiles && cp /tmp/pngout/*.png ~/SimularFiles/); ls ~/SimularFiles/ 2>/dev/null; ls /root/SimularFiles 2>/dev/null\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Copying rendered slides for review" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_109_20260923@113632690420.png", + "osworld_action": "WAIT" + }, + { + "step_num": 110, + "action": { + "id": "toolu_bdrk_01LeSCDkguF214jBwQqWmMKg", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"ls -d /root/SimularFiles /home/user/SimularFiles ~/SimularFiles 2>/dev/null; echo '--- whoami:'; whoami; echo HOME=$HOME; find / -maxdepth 3 -name SimularFiles -type d 2>/dev/null\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Locating the shared files directory" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_110_20260923@113650832084.png", + "osworld_action": "WAIT" + }, + { + "step_num": 111, + "action": { + "id": "toolu_bdrk_01KMuvqFaLqHHFzAmMtUz5ju", + "name": "execute", + "input": { + "code": "await chrome.activate()\nawait wait({ waitTime: 1 })\nawait machine.click({ x: 233, y: 615 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<10;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Checking for more instructions from Rachel" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_111_20260923@113735512478.png", + "osworld_action": "WAIT" + }, + { + "step_num": 112, + "action": { + "id": "toolu_bdrk_01St5XjboVPSLTUGzQNaNm9g", + "name": "execute", + "input": { + "code": "await machine.press({ key: 'pageUp' })\nawait machine.press({ key: 'pageUp' })\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Scrolling up to read all new instructions" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_112_20260923@113759054605.png", + "osworld_action": "WAIT" + }, + { + "step_num": 113, + "action": { + "id": "toolu_bdrk_012NTAFXGd1XcJgTZHMdjrwQ", + "name": "execute", + "input": { + "code": "await machine.press({ key: 'pageDown' })\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Reading the middle section of new messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_113_20260923@113814823552.png", + "osworld_action": "WAIT" + }, + { + "step_num": 114, + "action": { + "id": "toolu_bdrk_01LB12Qv35NCGrXfxFw3i6Lm", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cp ~/Desktop/AI-Assisted_Healthcare/AI-Assisted_Healthcare.pptx ~/Desktop/AI-Assisted_Healthcare.pptx && ls -la ~/Desktop/ && ls -la ~/Desktop/AI-Assisted_Healthcare/\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Placing the completed presentation on the Desktop" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_114_20260923@113832323785.png", + "osworld_action": "WAIT" + }, + { + "step_num": 115, + "action": { + "id": "toolu_bdrk_01YC5rrEuGAtLyRzQAaP4acA", + "name": "execute", + "input": { + "code": "await machine.click({ x: 470, y: 47 })\nawait wait({ waitTime: 3 })\nawait chrome.screenshot()\n", + "title": "Opening the GitLab tab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_115_20260923@113848700740.png", + "osworld_action": "WAIT" + }, + { + "step_num": 116, + "action": { + "id": "toolu_bdrk_015hfxSgqKyNyUuuXYioeVuT", + "name": "execute", + "input": { + "code": "await machine.click({ x: 994, y: 411 })\nawait machine.type({ text: 'user-d50jl7' })\nawait machine.click({ x: 994, y: 488 })\nawait machine.type({ text: '' })\nawait wait({ waitTime: 1 })\nawait machine.click({ x: 994, y: 593 })\nawait wait({ waitTime: 5 })\nawait chrome.screenshot()\n", + "title": "Signing in to GitLab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_116_20260923@113911945746.png", + "osworld_action": "WAIT" + }, + { + "step_num": 117, + "action": { + "id": "toolu_bdrk_01Rtdxk2BbCX5VGax9BMecZS", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1656, y: 397 }) // \"Never\" save password\nawait wait({ waitTime: 1 })\nawait machine.click({ x: 340, y: 90 }) // address bar\nawait machine.type({ text: 'http://gitlab.internal/projects/new#blank_project', withReturn: true })\nawait wait({ waitTime: 5 })\nawait chrome.screenshot()\n", + "title": "Opening the new project page in GitLab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_117_20260923@113940134478.png", + "osworld_action": "WAIT" + }, + { + "step_num": 118, + "action": { + "id": "toolu_bdrk_015Bm4i8iTLkT7cRrFKrFLLp", + "name": "execute", + "input": { + "code": "await machine.click({ x: 623, y: 355 })\nawait machine.type({ text: 'ai-healthcare-hw' })\nawait wait({ waitTime: 1 })\n// uncheck initialize with README\nawait machine.click({ x: 471, y: 713 })\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Entering the project name" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_118_20260923@113958365776.png", + "osworld_action": "WAIT" + }, + { + "step_num": 119, + "action": { + "id": "toolu_bdrk_01KgJG4wLWmJHXihLtxYvn89", + "name": "execute", + "input": { + "code": "await machine.click({ x: 836, y: 467 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Selecting the project namespace" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_119_20260923@114016988564.png", + "osworld_action": "WAIT" + }, + { + "step_num": 120, + "action": { + "id": "toolu_bdrk_01B7qLoDfdR8edMk4Hu7oXPY", + "name": "execute", + "input": { + "code": "await machine.click({ x: 667, y: 584 })\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()\n", + "title": "Choosing the user namespace" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_120_20260923@114031231364.png", + "osworld_action": "WAIT" + }, + { + "step_num": 121, + "action": { + "id": "toolu_bdrk_01XdqnDbdt32ebxpjSgQApjA", + "name": "execute", + "input": { + "code": "await machine.click({ x: 523, y: 901 })\nawait wait({ waitTime: 6 })\nawait chrome.screenshot()\n", + "title": "Creating the GitLab project" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_121_20260923@114052021751.png", + "osworld_action": "WAIT" + }, + { + "step_num": 122, + "action": { + "id": "toolu_bdrk_019YXxLgRN9z4imLkVnzXDZ6", + "name": "execute", + "input": { + "code": "var gitignore = `# Secrets - never commit credentials\n.env\n*.env\n!.env.example\n\n# Python\n__pycache__/\n*.py[cod]\n*.so\n.ipynb_checkpoints/\n\n# Model artefacts / generated output\n*.pkl\npredictions.csv\nwandb/\n\n# OS / Office junk\n.DS_Store\n~$*\nThumbs.db\n`\nvar readme = `# AI-Assisted Healthcare: Heart Disease Prediction Using Machine Learning\n\nCourse project (ML701) - heart-disease risk prediction with a Random Forest classifier.\n\n## Contents\n\n| File | Description |\n| --- | --- |\n| \\`model.py\\` | \\`HeartDiseaseModel\\` - Random Forest classifier plus the preprocessing pipeline (this repo's contribution). |\n| \\`run.py\\` | Training / evaluation driver written by Rachel. Only the \\`MAX_DEPTH\\` / \\`MAX_SAMPLES\\` values in the *Configurable Parameters* block were touched. |\n| \\`heart_train.csv\\` | Training split (734 records). |\n| \\`heart_test.csv\\` | Test split (184 records). |\n\n## Preprocessing\n\n* \\`StandardScaler\\` on \\`Age\\`, \\`RestingBP\\`, \\`Cholesterol\\`, \\`MaxHR\\`\n* \\`MinMaxScaler\\` on \\`Oldpeak\\`\n* Integer encoding for \\`Sex\\`, \\`ChestPainType\\`, \\`RestingECG\\`, \\`ExerciseAngina\\`, \\`ST_Slope\\`\n* \\`FastingBS\\` is already binary and is left untouched\n\nScalers/encoders are fitted on the training split only and re-used at inference\ntime (\\`preprocess_data(X, is_training=False)\\`).\n\n## Model\n\n\\`RandomForestClassifier(n_estimators=100, max_depth=8, max_samples=0.1, random_state=42, n_jobs=-1)\\`\n\n## Hyperparameter search\n\nGrid over the two configurable parameters (accuracy on \\`heart_test.csv\\`):\n\n| depth \\\\ samples | 0.1 | 0.3 | 0.5 | 0.7 | 0.9 |\n| --- | --- | --- | --- | --- | --- |\n| 4 | 0.8804 | 0.8696 | 0.8750 | 0.8750 | 0.8804 |\n| 5 | 0.8859 | 0.8750 | 0.8804 | 0.8641 | 0.8750 |\n| 6 | 0.8967 | 0.8913 | 0.8913 | 0.8804 | 0.8967 |\n| 7 | 0.8913 | 0.8804 | 0.8913 | 0.8913 | 0.8859 |\n| **8** | **0.9022** | 0.8967 | 0.8913 | 0.8967 | 0.8859 |\n| 9 | 0.8913 | 0.8859 | 0.8967 | 0.9022 | 0.8804 |\n| 10 | 0.8967 | 0.8859 | 0.9022 | 0.8859 | 0.8967 |\n\nBest accuracy 0.9022 is reached three times; the tie is broken towards the\nsmallest parameters, giving \\`MAX_DEPTH = 8\\`, \\`MAX_SAMPLES = 0.1\\`.\n\n## Results on the held-out test set\n\n| Metric | Value |\n| --- | --- |\n| Accuracy | 0.9022 |\n| Precision | 0.8889 |\n| Recall | 0.9412 |\n| F1-Score | 0.9143 |\n\n## Usage\n\n\\`\\`\\`bash\npip install pandas scikit-learn wandb\npython run.py\n\\`\\`\\`\n\n\\`run.py\\` logs to Weights & Biases. Export \\`WANDB_MODE=offline\\` if you do not\nhave a valid W&B key.\n\n> **Note:** \\`.env\\` is intentionally excluded from version control - it holds\n> cloud credentials and must never be committed.\n`\nawait machine.writeToFile({ text: gitignore, path: \"/home/user/Desktop/AI-Assisted_Healthcare/.gitignore\", overwrite: true })\nawait machine.writeToFile({ text: readme, path: \"/home/user/Desktop/AI-Assisted_Healthcare/README.md\", overwrite: true })\nvar r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && ls -la && echo '--- git ---' && git --version\" })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Adding README and gitignore for secrets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_122_20260923@114125657049.png", + "osworld_action": "WAIT" + }, + { + "step_num": 123, + "action": { + "id": "toolu_bdrk_01HjX9mdsyfPqjbkSXM75Z9D", + "name": "execute", + "input": { + "code": "var cmd = [\n \"cd ~/Desktop/AI-Assisted_Healthcare\",\n \"git init -q -b main 2>/dev/null || (git init -q && git checkout -q -b main)\",\n \"git config user.name 'user-d50jl7'\",\n \"git config user.email 'user-d50jl7@example.com'\",\n \"git add -A\",\n \"git status --short\",\n \"echo '--- staged files ---'\",\n \"git diff --cached --name-only\"\n].join(\" && \")\nvar r = await machine.exec({ command: cmd })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Initializing the local git repository" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_123_20260923@114149396408.png", + "osworld_action": "WAIT" + }, + { + "step_num": 124, + "action": { + "id": "toolu_bdrk_01ACsY3KRmtZUMcPVenT4G4q", + "name": "execute", + "input": { + "code": "var cmd = [\n \"cd ~/Desktop/AI-Assisted_Healthcare\",\n \"git commit -q -m 'Add Random Forest heart disease model and tuned hyperparameters' -m 'model.py implements HeartDiseaseModel (RandomForestClassifier) compatible with run.py. Tuned MAX_DEPTH=8 / MAX_SAMPLES=0.1 (test accuracy 0.9022). .env with cloud credentials is gitignored.'\",\n \"git log --oneline\",\n \"git remote remove origin 2>/dev/null; git remote add origin http://gitlab.internal/user-d50jl7/ai-healthcare-hw.git\",\n \"printf 'http://:@gitlab.internal\\\\n' > ~/.git-credentials\",\n \"chmod 600 ~/.git-credentials\",\n \"git config credential.helper store\",\n \"git ls-remote origin 2>&1 | head -5\"\n].join(\" && \")\nvar r = await machine.exec({ command: cmd, timeout: 90 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Committing the project and connecting the remote" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_124_20260923@114206153268.png", + "osworld_action": "WAIT" + }, + { + "step_num": 125, + "action": { + "id": "toolu_bdrk_012Jtijhn98VPaydqxneEvg7", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: \"cd ~/Desktop/AI-Assisted_Healthcare && git push -u origin main 2>&1 | tail -20\", timeout: 180 })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Pushing the project to GitLab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_125_20260923@114222217619.png", + "osworld_action": "WAIT" + }, + { + "step_num": 126, + "action": { + "id": "toolu_bdrk_01PiPbGCgquH2SZmi2caj1dB", + "name": "execute", + "input": { + "code": "await machine.click({ x: 340, y: 90 })\nawait machine.type({ text: 'http://gitlab.internal/user-d50jl7/ai-healthcare-hw', withReturn: true })\nawait wait({ waitTime: 6 })\nawait chrome.screenshot()\n", + "title": "Verifying the pushed repository in GitLab" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_126_20260923@114247119508.png", + "osworld_action": "WAIT" + }, + { + "step_num": 127, + "action": { + "id": "toolu_bdrk_01WSSaoYWqeCG3HWmAaSpnV8", + "name": "execute", + "input": { + "code": "await machine.click({ x: 220, y: 47 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<10;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Checking TeamChat for final updates" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_127_20260923@114309417211.png", + "osworld_action": "WAIT" + }, + { + "step_num": 128, + "action": { + "id": "toolu_bdrk_01K2cSRgDt2KpdiJ3pSV7R1N", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: [\n \"cd ~/Desktop/AI-Assisted_Healthcare\",\n \"rm -rf __pycache__\",\n \"echo '=== Desktop ==='\",\n \"ls -la ~/Desktop/\",\n \"echo '=== Project folder ==='\",\n \"ls -la .\",\n \"echo '=== PPT identical on Desktop? ==='\",\n \"md5sum AI-Assisted_Healthcare.pptx ~/Desktop/AI-Assisted_Healthcare.pptx\",\n \"echo '=== run.py diff vs original ==='\",\n \"diff /tmp/run.py.orig run.py; echo \\\"(diff rc=$?)\\\"\"\n].join(\" && \") })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Verifying files on Desktop and project folder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_128_20260923@114347546539.png", + "osworld_action": "WAIT" + }, + { + "step_num": 129, + "action": { + "id": "toolu_bdrk_01WYQSd5vPVk3XdaGLJ5tg9T", + "name": "execute", + "input": { + "code": "await wait({ waitTime: 120 })\nawait chrome.activate()\nawait machine.click({ x: 233, y: 615 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<10;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Waiting and re-checking TeamChat for updates" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_129_20260923@114602134026.png", + "osworld_action": "WAIT" + }, + { + "step_num": 130, + "action": { + "id": "toolu_bdrk_01GhMtaniNAbhq4Bebdmgttf", + "name": "execute", + "input": { + "code": "var machine = getMachine((await listMachines())[0].id)\nvar wins = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins))\n", + "title": "Reconnecting to the desktop session" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_130_20260923@114616662618.png", + "osworld_action": "WAIT" + }, + { + "step_num": 131, + "action": { + "id": "toolu_bdrk_013ENuv5ZvPJemfaAe3hpo3D", + "name": "execute", + "input": { + "code": "var chrome = await machine.getAppWindow('TeamChat - Google Chrome')\nawait chrome.activate()\nawait wait({ waitTime: 1 })\nawait machine.click({ x: 233, y: 615 })\nawait wait({ waitTime: 2 })\nawait machine.click({ x: 1170, y: 700 })\nfor (let i=0;i<10;i++){ await machine.press({ key: 'pageDown' }) }\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Checking TeamChat for the latest messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_131_20260923@114637806874.png", + "osworld_action": "WAIT" + }, + { + "step_num": 132, + "action": { + "id": "toolu_bdrk_018GSDAYnbAkK4vq9xePa7n7", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: [\n \"echo '=== ~/Desktop ==='\",\n \"ls -la ~/Desktop/\",\n \"echo\",\n \"echo '=== project folder ==='\",\n \"ls -a ~/Desktop/AI-Assisted_Healthcare/\",\n \"echo\",\n \"echo '=== git remote + tracked files ==='\",\n \"cd ~/Desktop/AI-Assisted_Healthcare && git remote -v && git ls-tree -r --name-only HEAD\"\n].join(\" && \") })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Final verification of files and repository" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_132_20260923@114654293021.png", + "osworld_action": "WAIT" + }, + { + "step_num": 133, + "action": { + "id": "toolu_bdrk_01CaapX165wJuncLwZqG2kgF", + "name": "execute", + "input": { + "code": "await wait({ waitTime: 90 })\nawait chrome.activate()\nawait wait({ waitTime: 1 })\nawait chrome.screenshot()\n", + "title": "Final check for any last TeamChat messages" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_133_20260923@114838063754.png", + "osworld_action": "WAIT" + }, + { + "step_num": 134, + "action": { + "id": "toolu_bdrk_01MDeMXTUtqCaj43Ko689GqA", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: [\n \"rm -f ~/.git-credentials\",\n \"cd ~/Desktop/AI-Assisted_Healthcare && git config --unset credential.helper\",\n \"echo '=== credentials removed ==='\",\n \"ls -la ~/.git-credentials 2>&1 | tail -1\",\n \"echo '=== git log ==='\",\n \"git log --oneline --stat | head -15\"\n].join(\"; \") })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Removing stored credentials from disk" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_134_20260923@114852763144.png", + "osworld_action": "WAIT" + }, + { + "step_num": 135, + "action": { + "id": "toolu_bdrk_01BnPq9KxVBQZ5ZV3xnfka1f", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare.pptx')\nprint('slides:', len(p.slides))\nfor idx in [5,6,8,9,11]:\n print('--- SLIDE', idx)\n for sh in p.slides[idx-1].shapes:\n if sh.has_table:\n for row in sh.table.rows: print(' ', ' | '.join(c.text for c in row.cells))\n elif sh.has_text_frame and 'highest accuracy' in sh.text_frame.text:\n print(' TEXT:', sh.text_frame.text.split('.')[-3].strip()+'.')\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Final verification of the Desktop presentation" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_135_20260923@114906233609.png", + "osworld_action": "WAIT" + }, + { + "step_num": 136, + "action": { + "id": "toolu_bdrk_019rruGXuFfoSbKeZ7NTWS6Z", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF' 2>&1 | grep -viE 'numpy|warnings.warn'\nfrom pptx import Presentation\np=Presentation('/home/user/Desktop/AI-Assisted_Healthcare.pptx')\nfor sh in p.slides[7].shapes:\n if sh.has_text_frame and sh.text_frame.text.strip():\n print('>>>', sh.text_frame.text)\n print()\nEOF` })\nconsole.log(r.stdout, r.stderr)\n", + "title": "Confirming slide 8 results text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_136_20260923@114919141705.png", + "osworld_action": "WAIT" + }, + { + "step_num": 137, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_137_20260923@114942438162.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/027.json b/demo/data/official/sai_traj/027.json new file mode 100644 index 0000000..529a647 --- /dev/null +++ b/demo/data/official/sai_traj/027.json @@ -0,0 +1,453 @@ +{ + "task": "027", + "run": "v21-ref5-recheck-a-20260923T103950Z", + "score": 1.0, + "mean": 0.3333, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref5-recheck-a-20260923T103950Z/027/traj.jsonl", + "fetched_at": "2026-10-26T06:55:18Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_0161sFGCfNwqYrBTpMKHmE87", + "name": "execute", + "input": { + "title": "Checking the machine and looking for the spreadsheet files", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar r = await machine.exec({ command: \"ls -la ~/ ~/Desktop 2>/dev/null; find / -name 'daily.xlsx' -o -name 'summary.xlsx' 2>/dev/null | head -50\" })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@104135627317.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_014Kgb2ziwE7tUHYvjHRJGzT", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 -c \"\nimport openpyxl\nfor f in ['/home/user/Desktop/daily.xlsx','/home/user/Desktop/summary.xlsx']:\n wb = openpyxl.load_workbook(f)\n print('=== ', f)\n for ws in wb.worksheets:\n print('--- sheet:', ws.title, ws.dimensions, ws.max_row, ws.max_column)\n for row in ws.iter_rows(min_row=1, max_row=min(ws.max_row,40), values_only=True):\n print(row)\n\"` })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)", + "title": "Reading the structure of both spreadsheets" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@104147192532.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01VmX12tbywbCek2ECrrBEAF", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && rm -rf xl1 && mkdir xl1 && cd xl1 && unzip -o -q /home/user/Desktop/daily.xlsx && ls -R . | head -40 && echo '=== sharedStrings' && (cat xl/sharedStrings.xml 2>/dev/null | head -c 3000) && echo && echo '=== sheet1' && (cat xl/worksheets/sheet1.xml | head -c 4000)` })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)", + "title": "Extracting the daily.xlsx contents to inspect it" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@104158593367.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01Svf2LJXpKS8FFy5uKby8fu", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && rm -rf xl2 && mkdir xl2 && cd xl2 && unzip -o -q /home/user/Desktop/summary.xlsx && echo '=== sharedStrings' && (cat xl/sharedStrings.xml 2>/dev/null) && echo && echo '=== sheet1' && (cat xl/worksheets/sheet1.xml) && echo && echo '=== workbook' && cat xl/workbook.xml` })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)", + "title": "Inspecting the summary spreadsheet layout" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@104210120317.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_018mTmwyR3S766ymXcVNZ4qR", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp/xl1 && python3 - <<'EOF'\nimport re,xml.etree.ElementTree as ET\nns={'m':'http://schemas.openxmlformats.org/spreadsheetml/2006/main'}\nss=[ (''.join(t.text or '' for t in si.iter('{http://schemas.openxmlformats.org/spreadsheetml/2006/main}t'))) for si in ET.parse('xl/sharedStrings.xml').getroot()]\nprint(\"SHARED:\", ss)\nroot=ET.parse('xl/worksheets/sheet1.xml').getroot()\nfor row in root.iter('{http://schemas.openxmlformats.org/spreadsheetml/2006/main}row'):\n out=[]\n for c in row:\n v=c.find('{http://schemas.openxmlformats.org/spreadsheetml/2006/main}v')\n val=v.text if v is not None else None\n if c.get('t')=='s' and val is not None: val=ss[int(val)]\n out.append((c.get('r'), c.get('t'), c.get('s'), val))\n print(out)\nEOF` })\nconsole.log(r.stdout.slice(-4000))\nconsole.log(\"ERR\", r.stderr)", + "title": "Reading the exact date labels in daily.xlsx" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@104233110549.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_01LtPKmq4mVEfoQWHyi62rw6", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `curl -s -m 25 -A \"Mozilla/5.0 (Windows NT 10.0; Win64; x64)\" \"https://query1.finance.yahoo.com/v8/finance/chart/NVDA?period1=1735689600&period2=1751328000&interval=1d&events=div%2Csplit\" -o /tmp/nvda.json -w \"HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; head -c 600 /tmp/nvda.json`, timeout: 40 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Testing network access to a financial data source" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@104245143868.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01Sz7qYmHzNLZTxQkqjMik4B", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && for T in NVDA AAPL WMT AMZN META; do curl -s -m 30 -A \"Mozilla/5.0 (Windows NT 10.0; Win64; x64)\" \"https://query1.finance.yahoo.com/v8/finance/chart/$T?period1=1735603200&period2=1751414400&interval=1d\" -o /tmp/y_$T.json -w \"$T HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; sleep 1; done`, timeout: 180 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Downloading historical data for all five companies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@104306068843.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_0141GWEomyWZADpiTGd6Ydim", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json, datetime, csv, os\ntick=['NVDA','AAPL','WMT','AMZN','META']\nalld={}\nfor t in tick:\n d=json.load(open(f'/tmp/y_{t}.json'))\n res=d['chart']['result'][0]\n ts=res['timestamp']; q=res['indicators']['quote'][0]\n gmtoff=res['meta'].get('gmtoffset',0)\n rows=[]\n for i,s in enumerate(ts):\n dt=datetime.datetime.utcfromtimestamp(s+gmtoff).date()\n rows.append(dict(date=dt.isoformat(), open=q['open'][i], high=q['high'][i],\n low=q['low'][i], close=q['close'][i], volume=q['volume'][i]))\n alld[t]=rows\n print(t, 'n=',len(rows), 'first',rows[0]['date'], 'last',rows[-1]['date'])\njson.dump(alld, open('/tmp/alld.json','w'))\n# write csv for inspection\nfor t in tick:\n with open(f'/tmp/{t}.csv','w',newline='') as f:\n w=csv.DictWriter(f, fieldnames=['date','open','high','low','close','volume']); w.writeheader(); w.writerows(alld[t])\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Parsing the downloaded price data" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@104319552958.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_01Ao7M1kkaiNMjBATVAhDUz1", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json\nalld=json.load(open('/tmp/alld.json'))\nW0,W1='2025-03-19','2025-04-16'\nfor t,rows in alld.items():\n w=[x for x in rows if W0<=x['date']<=W1]\n print('===',t,'window rows:',len(w))\n for x in w: print(x['date'], 'O=%.4f H=%.4f L=%.4f C=%.4f V=%d'%(x['open'],x['high'],x['low'],x['close'],x['volume']))\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Reviewing the 21-day window data for each company" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@104333195180.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_01WvWHZ2w2rMTieaj9g2TDDe", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json, math\nalld=json.load(open('/tmp/alld.json'))\nW0,W1='2025-03-19','2025-04-16'\nH0,H1='2025-01-02','2025-06-30'\nnames={'NVDA':'Nvidia','AAPL':'Apple','WMT':'Walmart','AMZN':'Amazon','META':'Meta'}\nres={}\nfor t,rows in alld.items():\n w=[x for x in rows if W0<=x['date']<=W1]\n h=[x for x in rows if H0<=x['date']<=H1]\n assert len(w)==21, (t,len(w))\n opens=[x['open'] for x in w]\n wl=min(x['low'] for x in w); wh=max(x['high'] for x in w)\n fl=min(x['low'] for x in h); fh=max(x['high'] for x in h)\n n=len(opens); mu=sum(opens)/n\n std=math.sqrt(sum((o-mu)**2 for o in opens)/n) # population\n vwap=sum(x['open']*x['volume'] for x in w)/sum(x['volume'] for x in w)\n cpc=(w[-1]['close']-w[0]['close'])/w[0]['close']*100\n adr=sum(x['high']-x['low'] for x in w)/n\n res[t]=dict(company=names[t], n_half=len(h), Window_Low=wl, Window_High=wh,\n FirstHalf_Low=fl, FirstHalf_High=fh, Low_Ratio=wl/fl, High_Ratio=wh/fh,\n Open_STD=std, VWAP_Open=vwap, Close_PctChange=cpc, Avg_Daily_Range=adr,\n opens=opens)\n # find dates of extremes for verification\n fld=[x['date'] for x in h if x['low']==fl][0]; fhd=[x['date'] for x in h if x['high']==fh][0]\n wld=[x['date'] for x in w if x['low']==wl][0]; whd=[x['date'] for x in w if x['high']==wh][0]\n print(t, 'halfN=',len(h), 'FH_Low',fl,fld,'FH_High',fh,fhd,'| W_Low',wl,wld,'W_High',wh,whd)\njson.dump({k:{kk:vv for kk,vv in v.items()} for k,v in res.items()}, open('/tmp/res.json','w'))\nprint()\n# dense rank on rounded High_Ratio, 1 = largest\nhr={t: round(res[t]['High_Ratio'],2) for t in res}\nuniq=sorted(set(hr.values()), reverse=True)\nrank={v:i+1 for i,v in enumerate(uniq)}\nprint('%-8s %10s %10s %10s %10s %8s %8s %9s %10s %9s %9s %5s'%('Comp','WLow','WHigh','FHLow','FHHigh','LowR','HighR','OpenSTD','VWAP','ClosePct','AvgRange','Rk'))\norder=['NVDA','AAPL','WMT','AMZN','META']\nout={}\nfor t in order:\n v=res[t]\n row=dict(Window_Low=round(v['Window_Low'],2), Window_High=round(v['Window_High'],2),\n FirstHalf_Low=round(v['FirstHalf_Low'],2), FirstHalf_High=round(v['FirstHalf_High'],2),\n Low_Ratio=round(v['Low_Ratio'],2), High_Ratio=round(v['High_Ratio'],2),\n Open_STD=round(v['Open_STD'],2), VWAP_Open=round(v['VWAP_Open'],2),\n Close_PctChange=round(v['Close_PctChange'],2), Avg_Daily_Range=round(v['Avg_Daily_Range'],2),\n Rank_High_Ratio=rank[hr[t]])\n out[names[t]]=row\n print('%-8s %10.2f %10.2f %10.2f %10.2f %8.2f %8.2f %9.2f %10.2f %9.2f %9.2f %5d'%(names[t],\n row['Window_Low'],row['Window_High'],row['FirstHalf_Low'],row['FirstHalf_High'],row['Low_Ratio'],\n row['High_Ratio'],row['Open_STD'],row['VWAP_Open'],row['Close_PctChange'],row['Avg_Daily_Range'],row['Rank_High_Ratio']))\nprint()\nprint('raw High_Ratio:', {t: round(res[t]['High_Ratio'],6) for t in order})\njson.dump(out, open('/tmp/summary_out.json','w'), indent=1)\njson.dump({names[t]: [round(o,2) for o in res[t]['opens']] for t in order}, open('/tmp/daily_out.json','w'), indent=1)\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Computing all summary metrics for the five companies" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@104405516257.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01NMspzu2aWwtTVZ1bY3wtB4", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && for T in nvda aapl wmt amzn meta; do curl -s -m 25 \"https://stooq.com/q/d/l/?s=$T.us&d1=20250101&d2=20250701&i=d\" -o /tmp/s_$T.csv -w \"$T HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; done; head -3 /tmp/s_nvda.csv`, timeout: 180 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Fetching the same data from a second source for verification" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@104435272818.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_01RGBz7MJb6ytr8ecvRPfiq4", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `curl -s -m 25 -H \"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 Chrome/120 Safari/537.36\" -H \"Accept: application/json\" \"https://api.nasdaq.com/api/quote/NVDA/historical?assetclass=stocks&fromdate=2025-03-15&todate=2025-04-17&limit=40\" -o /tmp/nq.json -w \"HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; head -c 1200 /tmp/nq.json`, timeout: 60 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Trying Nasdaq as an alternate verification source" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@104450731657.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01BJVYr8CfZ69E9HFWL8so4d", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `curl -sL -m 30 -A \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 Chrome/122 Safari/537.36\" \"https://www.wsj.com/market-data/quotes/NVDA/historical-prices/download?MOD=mw_quote&startDate=03/19/2025&endDate=04/16/2025\" -o /tmp/wsj.csv -w \"HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; head -c 900 /tmp/wsj.csv; echo; echo \"--- alt: marketwatch\"; curl -sL -m 30 -A \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 Chrome/122 Safari/537.36\" \"https://www.marketwatch.com/investing/stock/nvda/downloaddatapartial?startdate=03/19/2025%2000:00:00&enddate=04/16/2025%2023:59:59&daterange=d30&frequency=p1d&csvdownload=true&downloadpartial=false&newdates=false\" -o /tmp/mw.csv -w \"HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; head -c 900 /tmp/mw.csv`, timeout: 120 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Trying WSJ and MarketWatch for independent price verification" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@104514203727.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01DcKcYVVBSyD6PrU1wwgBLR", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `echo \"--- stockanalysis\"; curl -sL -m 25 -A \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/122 Safari/537.36\" \"https://stockanalysis.com/api/symbol/s/nvda/history?period=Daily&range=1Y\" -o /tmp/sa.json -w \"HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; head -c 400 /tmp/sa.json; echo; echo \"--- twelvedata demo\"; curl -sL -m 25 \"https://api.twelvedata.com/time_series?symbol=NVDA&interval=1day&start_date=2025-04-07&end_date=2025-04-09&apikey=demo\" -o /tmp/td.json -w \"HTTP:%{http_code}\\\\n\"; head -c 600 /tmp/td.json`, timeout: 120 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Trying additional data sources for cross-verification" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@104531238758.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01ErURmbtstKRf8naXae2jst", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && for T in nvda aapl wmt amzn meta; do curl -sL -m 25 -A \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/122 Safari/537.36\" \"https://stockanalysis.com/api/symbol/s/$T/history?period=Daily&range=5Y\" -o /tmp/sa_$T.json -w \"$T HTTP:%{http_code} SIZE:%{size_download}\\\\n\"; sleep 1; done; python3 -c \"\nimport json\nd=json.load(open('/tmp/sa_nvda.json'))['data']\nprint('rows',len(d),'oldest',d[-1]['t'],'newest',d[0]['t'])\n\"`, timeout: 180 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Downloading five-year history from the verification source" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@104550040323.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01NZViMKmtGYmDxoUUeBo5rM", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json\nd=json.load(open('/tmp/sa_nvda.json'))\nprint('keys:', list(d.keys()))\nprint('sample row:', d['data'][0])\nrow=[x for x in d['data'] if x['t']=='2025-04-07'][0]\nprint('2025-04-07:', row)\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Checking the field format of the verification data" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@104601716946.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01Fo7XeZn5JEtVEYK9KoJvBX", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json\nalld=json.load(open('/tmp/alld.json'))\ntick=['NVDA','AAPL','WMT','AMZN','META']\nsa={}\nfor t in tick:\n d=json.load(open(f'/tmp/sa_{t.lower()}.json'))['data']\n sa[t]={x['t']:x for x in d}\nW0,W1='2025-03-19','2025-04-16'\nH0,H1='2025-01-02','2025-06-30'\ndiffs=0\nfor t in tick:\n print('====',t)\n for x in alld[t]:\n if not (H0<=x['date']<=H1): continue\n s=sa[t].get(x['date'])\n if s is None: print(' MISSING in sa:', x['date']); continue\n d=[]\n for k,kk in (('open','o'),('high','h'),('low','l'),('close','c')):\n if abs(x[k]-s[kk])>0.015: d.append(f\"{k}: Y={x[k]:.4f} S={s[kk]:.4f}\")\n if abs(x['volume']-s['v'])/max(s['v'],1)>0.02: d.append(f\"vol: Y={x['volume']} S={s['v']}\")\n if d:\n diffs+=1\n inw = 'IN-WINDOW' if W0<=x['date']<=W1 else ''\n print(' ',x['date'], inw, ' | '.join(d))\nprint('total differing days:',diffs)\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Comparing both data sources day by day" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@104619854439.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01PM26bJpHwrcuFhpFgxtomc", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 -c \"\nimport json\ns=json.load(open('/tmp/summary_out.json')); d=json.load(open('/tmp/daily_out.json'))\ncols=['Window_Low','Window_High','FirstHalf_Low','FirstHalf_High','Low_Ratio','High_Ratio','Open_STD','VWAP_Open','Close_PctChange','Avg_Daily_Range','Rank_High_Ratio']\nprint('%-9s'%'Company'+''.join('%16s'%c for c in cols))\nfor c in ['Nvidia','Apple','Walmart','Amazon','Meta']:\n print('%-9s'%c+''.join('%16s'%s[c][k] for k in cols))\nprint()\nprint('DAILY OPENS (2025-03-19 .. 2025-04-16):')\nfor c in ['Nvidia','Apple','Walmart','Amazon','Meta']: print('%-8s'%c, d[c])\n\"` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Displaying the final computed values" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@104636720762.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_016Ufpz96epmUAscvmH7fCX5", + "name": "execute", + "input": { + "code": "var results = {\n dailyOpens: {\n Nvidia: [117.27,116.55,116.94,119.88,120.55,118.73,111.35,111.49,105.13,108.52,107.29,103.51,98.91,87.46,103.81,98.89,109.37,108.5,114.11,110.97,104.55],\n Apple: [214.22,213.99,211.56,221.0,220.77,223.51,221.39,221.67,217.01,219.81,221.32,205.54,193.89,177.2,186.7,171.95,189.07,186.1,211.44,201.86,198.36],\n Walmart: [85.95,85.81,85.28,86.47,86.76,85.19,84.78,85.43,84.76,87.54,88.2,86.28,85.64,80.24,85.85,83.33,88.73,90.68,93.27,94.82,94.0],\n Amazon: [193.38,193.07,192.9,200.0,203.6,205.84,200.89,198.42,188.19,187.86,187.66,183.0,167.15,162.0,185.23,172.12,185.44,179.93,186.84,181.41,176.29],\n Meta: [584.28,582.76,583.42,614.97,626.76,624.89,602.0,600.31,563.5,570.84,574.91,546.22,506.62,485.1,543.25,509.27,575.49,535.51,556.17,532.11,508.51]\n },\n summary: {\n Nvidia: [86.62,122.22,86.62,158.71,1.0,0.77,7.98,106.68,-11.09,6.33,4],\n Apple: [169.21,225.62,169.21,250.0,1.0,0.90,15.79,198.31,-9.74,9.31,2],\n Walmart: [79.81,96.03,79.81,105.30,1.0,0.91,3.46,86.77,5.63,3.01,1],\n Amazon: [161.38,206.21,161.38,242.52,1.0,0.85,11.33,183.02,-10.85,8.82,3],\n Meta: [481.90,633.88,479.80,747.90,1.0,0.85,39.44,552.87,-14.00,27.66,3]\n }\n}\nvar w = await machine.listAppWindows()\nconsole.log(JSON.stringify(w, null, 1))\nvar r2 = await machine.exec({ command: \"ps aux | grep -i office | grep -v grep | head -5; ls -la /home/user/Desktop/\" })\nconsole.log(r2.stdout)", + "title": "Saving results and checking for open LibreOffice windows" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@104700327795.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_016ioTcaa2fN5qabx5mxM4MD", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `python3 -c \"import uno; print('uno OK')\" 2>&1 | tail -2; echo \"---\"; ls /usr/lib/libreoffice/program/ | grep -i -E \"^python|uno\" | head; echo \"--- pip try\"; (pip3 install openpyxl 2>&1 | tail -4); echo \"--- recheck\"; python3 -c \"import openpyxl; print('openpyxl', openpyxl.__version__)\" 2>&1 | tail -2`, timeout: 120 })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Checking for spreadsheet libraries and LibreOffice scripting" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@104724360853.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01Vd88c47q7A2yYnTcv31m6W", + "name": "execute", + "input": { + "code": "var daily = await machine.getAppWindow(\"daily.xlsx - LibreOffice Calc\")\nawait daily.activate()\nawait wait({ waitTime: 1 })\nawait daily.screenshot()", + "title": "Viewing the currently open daily.xlsx window" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@104750536438.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_013DVfHLKN6JXbVn5qPbGR2r", + "name": "execute", + "input": { + "code": "await daily.press({ key: 'ctrl+q' })\nawait wait({ waitTime: 3 })\nvar w2 = await machine.listAppWindows()\nconsole.log(\"windows:\", JSON.stringify(w2))\nvar r = await machine.exec({ command: \"ps aux | grep -i soffice | grep -v grep | wc -l; ls -la /home/user/Desktop/\" })\nconsole.log(r.stdout)", + "title": "Quitting LibreOffice to release the files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@104813374929.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_016nWkZ1jkEB8ng1MbAAzu8K", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json, math, openpyxl\nalld=json.load(open('/tmp/alld.json'))\nW0,W1='2025-03-19','2025-04-16'\nH0,H1='2025-01-02','2025-06-30'\nnames={'NVDA':'Nvidia','AAPL':'Apple','WMT':'Walmart','AMZN':'Amazon','META':'Meta'}\norder=['NVDA','AAPL','WMT','AMZN','META']\nM={}\nfor t in order:\n rows=alld[t]\n w=[x for x in rows if W0<=x['date']<=W1]; h=[x for x in rows if H0<=x['date']<=H1]\n assert len(w)==21 and len(h)==122\n o=[x['open'] for x in w]; n=len(o); mu=sum(o)/n\n wl=min(x['low'] for x in w); wh=max(x['high'] for x in w)\n fl=min(x['low'] for x in h); fh=max(x['high'] for x in h)\n M[t]=dict(dates=[x['date'] for x in w], opens=o,\n Window_Low=wl, Window_High=wh, FirstHalf_Low=fl, FirstHalf_High=fh,\n Low_Ratio=wl/fl, High_Ratio=wh/fh,\n Open_STD=math.sqrt(sum((v-mu)**2 for v in o)/n),\n VWAP_Open=sum(x['open']*x['volume'] for x in w)/sum(x['volume'] for x in w),\n Close_PctChange=(w[-1]['close']-w[0]['close'])/w[0]['close']*100,\n Avg_Daily_Range=sum(x['high']-x['low'] for x in w)/n)\nhr={t: round(M[t]['High_Ratio'],2) for t in order}\nuniq=sorted(set(hr.values()), reverse=True); rk={v:i+1 for i,v in enumerate(uniq)}\n\n# ---------- daily.xlsx ----------\nwb=openpyxl.load_workbook('/home/user/Desktop/daily.xlsx'); ws=wb.active\nhdr=[ws.cell(row=1,column=c).value for c in range(1,7)]\nprint('daily header:', hdr)\nxl_dates=[ws.cell(row=r,column=1).value for r in range(2,23)]\nprint('daily A col:', xl_dates)\n# map header name -> ticker\nn2t={v:k for k,v in names.items()}\nfor c in range(2,7):\n comp=str(ws.cell(row=1,column=c).value).strip(); t=n2t[comp]\n for i in range(21):\n # verify date alignment\n y,m,d=[int(z) for z in str(xl_dates[i]).split('-')]\n assert f'{y:04d}-{m:02d}-{d:02d}'==M[t]['dates'][i], (comp,xl_dates[i],M[t]['dates'][i])\n cell=ws.cell(row=2+i, column=c)\n cell.value=round(M[t]['opens'][i],2)\n cell.number_format='0.00'\nwb.save('/home/user/Desktop/daily.xlsx')\nprint('daily.xlsx saved')\n\n# ---------- summary.xlsx ----------\nwb2=openpyxl.load_workbook('/home/user/Desktop/summary.xlsx'); ws2=wb2.active\ncols=[str(ws2.cell(row=1,column=c).value).strip() for c in range(1,ws2.max_column+1)]\nprint('summary header:', cols)\nfor r in range(2,7):\n comp=str(ws2.cell(row=r,column=1).value).strip(); t=n2t[comp]\n vals=dict(\n Window_Low=round(M[t]['Window_Low'],2), Window_High=round(M[t]['Window_High'],2),\n FirstHalf_Low=round(M[t]['FirstHalf_Low'],2), FirstHalf_High=round(M[t]['FirstHalf_High'],2),\n Low_Ratio=round(M[t]['Low_Ratio'],2), High_Ratio=round(M[t]['High_Ratio'],2),\n Open_STD=round(M[t]['Open_STD'],2), VWAP_Open=round(M[t]['VWAP_Open'],2),\n Close_PctChange=round(M[t]['Close_PctChange'],2),\n Avg_Daily_Range=round(M[t]['Avg_Daily_Range'],2),\n Rank_High_Ratio=rk[hr[t]])\n for ci,cn in enumerate(cols, start=1):\n if cn in vals:\n cell=ws2.cell(row=r, column=ci); cell.value=vals[cn]\n cell.number_format='0' if cn=='Rank_High_Ratio' else '0.00'\n print(comp, vals)\nwb2.save('/home/user/Desktop/summary.xlsx')\nprint('summary.xlsx saved')\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Writing opening prices and summary metrics into the files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@104853449218.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01NeFay6hGL2KFWKr45qnJeb", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport openpyxl\nwb=openpyxl.load_workbook('/home/user/Desktop/daily.xlsx', data_only=True); ws=wb.active\nprint('=== daily.xlsx', ws.dimensions)\nfor row in ws.iter_rows(min_row=1,max_row=ws.max_row,max_col=6,values_only=True): print(row)\nwb2=openpyxl.load_workbook('/home/user/Desktop/summary.xlsx', data_only=True); ws2=wb2.active\nprint('=== summary.xlsx', ws2.dimensions)\nfor row in ws2.iter_rows(min_row=1,max_row=ws2.max_row,max_col=12,values_only=True): print(row)\nEOF` })\nconsole.log(r.stdout)\nconsole.log(\"ERR\", r.stderr)", + "title": "Verifying the saved contents of both files" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@104920739632.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_01NHbpjjFZkUJridLYXNNntU", + "name": "execute", + "input": { + "code": "var r = await machine.exec({ command: `cd /tmp && python3 - <<'EOF'\nimport json\nalld=json.load(open('/tmp/alld.json'))\nW0,W1='2025-03-19','2025-04-16'; H0,H1='2025-01-02','2025-06-30'\nfor t in ['NVDA','AAPL','WMT','AMZN','META']:\n w=[x for x in alld[t] if W0<=x['date']<=W1]; h=[x for x in alld[t] if H0<=x['date']<=H1]\n wh=max(w,key=lambda x:x['high']); wl=min(w,key=lambda x:x['low'])\n fh=max(h,key=lambda x:x['high']); fl=min(h,key=lambda x:x['low'])\n print(f\"{t:5} W_High {wh['high']:8.2f} @{wh['date']} W_Low {wl['low']:8.2f} @{wl['date']} FH_High {fh['high']:8.2f} @{fh['date']} FH_Low {fl['low']:8.2f} @{fl['date']} C_first {w[0]['close']:.2f} C_last {w[-1]['close']:.2f}\")\nEOF\nsetsid soffice --calc /home/user/Desktop/daily.xlsx /home/user/Desktop/summary.xlsx >/dev/null 2>&1 &\nsleep 1; echo launched`, timeout: 60 })\nconsole.log(r.stdout)\nawait wait({ waitTime: 12 })\nvar w3 = await machine.listAppWindows()\nconsole.log(\"windows:\", JSON.stringify(w3))", + "title": "Recording extreme dates and reopening the files in LibreOffice" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@105019621649.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_01NWPK5Eo4HXpTa58wiipjWx", + "name": "execute", + "input": { + "code": "var dailyW = await machine.getAppWindow(\"daily.xlsx - LibreOffice Calc\")\nawait dailyW.activate()\nawait wait({ waitTime: 2 })\nawait dailyW.screenshot()", + "title": "Checking the filled daily opening prices on screen" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@105040870836.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_01XsTvTLkjdB2g3F3WRSdVPP", + "name": "execute", + "input": { + "code": "var sumW = await machine.getAppWindow(\"summary.xlsx - LibreOffice Calc\")\nawait sumW.activate()\nawait wait({ waitTime: 2 })\nawait sumW.screenshot()", + "title": "Checking the completed summary metrics on screen" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@105100684974.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01NQ3CjXGxBTDpcDtc8Xm2XF", + "name": "execute", + "input": { + "code": "await dailyW.activate()\nawait wait({ waitTime: 2 })\nawait dailyW.screenshot()", + "title": "Final visual confirmation of daily.xlsx" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@105117870989.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01ATx8bF3zfQt5biSCVFemcG", + "name": "execute", + "input": { + "code": "await sumW.activate()\nawait wait({ waitTime: 2 })\nawait sumW.screenshot()", + "title": "Final visual confirmation of summary.xlsx" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@105132064754.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": "DONE", + "reward": 0, + "done": true, + "screenshot_file": "step_30_20260923@105156047736.png", + "osworld_action": null + } + ] +} diff --git a/demo/data/official/sai_traj/034.json b/demo/data/official/sai_traj/034.json new file mode 100644 index 0000000..e72233e --- /dev/null +++ b/demo/data/official/sai_traj/034.json @@ -0,0 +1,1572 @@ +{ + "task": "034", + "run": "v21-ref5-rest-20260923T103950Z", + "score": 0.875, + "mean": 0.875, + "source_url": "https://huggingface.co/datasets/simular-ai/sai-osworld-v21-reference-runs/resolve/main/runs/v21-ref5-rest-20260923T103950Z/034/traj.jsonl", + "fetched_at": "2026-10-26T06:55:19Z", + "steps": [ + { + "step_num": 1, + "action": { + "id": "toolu_bdrk_01VRc76HAJs1bq68VFK2iBum", + "name": "execute", + "input": { + "title": "Looking for the documents on the machine", + "code": "var machine = getMachine((await listMachines())[0].id)\nvar r = await machine.exec({ command: \"ls -la ~/ ~/Desktop ~/Documents ~/Downloads 2>/dev/null\" })\nconsole.log(r.stdout)\nconsole.log(\"STDERR:\", r.stderr)" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_1_20260923@111722705940.png", + "osworld_action": "WAIT" + }, + { + "step_num": 2, + "action": { + "id": "toolu_bdrk_01CaznWeRaqGf5oCQDGUnGyo", + "name": "execute", + "input": { + "code": "var wins = await machine.listAppWindows()\nconsole.log(JSON.stringify(wins, null, 2))\nvar r2 = await machine.exec({ command: \"ls -laR ~/Desktop/HKU-RIMS-System | head -100\" })\nconsole.log(r2.stdout)", + "title": "Checking open windows and the RIMS system folder" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_2_20260923@111738546976.png", + "osworld_action": "WAIT" + }, + { + "step_num": 3, + "action": { + "id": "toolu_bdrk_01JGJUCPdkULKkJXFpKFQ2Jq", + "name": "execute", + "input": { + "code": "var r3 = await machine.exec({ command: \"find / -name 'Guidelines.docx' -o -name 'proposal.docx' 2>/dev/null | head -20\" })\nconsole.log(r3.stdout)\nvar r4 = await machine.exec({ command: \"cat ~/Desktop/HKU-RIMS-System/README.md\" })\nconsole.log(r4.stdout)", + "title": "Locating the Word documents and reading the system README" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_3_20260923@111752907879.png", + "osworld_action": "WAIT" + }, + { + "step_num": 4, + "action": { + "id": "toolu_bdrk_01Tg6ZFSkrrgXNjGZBAp6Yn9", + "name": "execute", + "input": { + "code": "var r5 = await machine.exec({ command: \"cd /tmp && rm -rf gdoc && mkdir gdoc && cd gdoc && unzip -o ~/Desktop/Guidelines.docx > /dev/null && ls -la word/\" })\nconsole.log(r5.stdout, r5.stderr)\nvar r6 = await machine.exec({ command: \"python3 -c \\\"import docx; print('ok')\\\" 2>&1\" })\nconsole.log(r6.stdout, r6.stderr)", + "title": "Unpacking the Guidelines document" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_4_20260923@111803342655.png", + "osworld_action": "WAIT" + }, + { + "step_num": 5, + "action": { + "id": "toolu_bdrk_016MsCsTcQmVH5Frwc1iUMii", + "name": "execute", + "input": { + "code": "var script = `\nimport sys, re, zipfile\nfrom xml.etree import ElementTree as ET\nW='{http://schemas.openxmlformats.org/wordprocessingml/2006/main}'\npath=sys.argv[1]\nz=zipfile.ZipFile(path)\nxml=z.read('word/document.xml')\nroot=ET.fromstring(xml)\ndef para_text(p):\n out=[]\n for node in p.iter():\n if node.tag==W+'t':\n out.append(node.text or '')\n elif node.tag==W+'tab':\n out.append('\\\\t')\n elif node.tag==W+'br':\n out.append('\\\\n')\n return ''.join(out)\nbody=root.find(W+'body')\ndef walk(el, depth=0):\n for child in el:\n if child.tag==W+'p':\n print(para_text(child))\n elif child.tag==W+'tbl':\n print('---TABLE START---')\n for tr in child.findall(W+'tr'):\n cells=[]\n for tc in tr.findall(W+'tc'):\n ct=' '.join(para_text(p) for p in tc.findall(W+'p'))\n cells.append(ct.strip())\n print(' | '.join(cells))\n print('---TABLE END---')\n else:\n walk(child, depth+1)\nwalk(body)\n`;\nawait machine.writeToFile({ text: script, path: \"/tmp/dx.py\", overwrite: true })\nvar g = await machine.exec({ command: \"python3 /tmp/dx.py ~/Desktop/Guidelines.docx\" })\nconsole.log(g.stdout)\nconsole.log(\"ERR\", g.stderr)", + "title": "Extracting text from Guidelines.docx" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_5_20260923@111818108815.png", + "osworld_action": "WAIT" + }, + { + "step_num": 6, + "action": { + "id": "toolu_bdrk_01WEaJQM77AhsR1QAemQr93W", + "name": "execute", + "input": { + "code": "var p = await machine.exec({ command: \"python3 /tmp/dx.py ~/Desktop/proposal.docx\" })\nconsole.log(p.stdout)\nconsole.log(\"ERR\", p.stderr)", + "title": "Extracting text from proposal.docx" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_6_20260923@111829005093.png", + "osworld_action": "WAIT" + }, + { + "step_num": 7, + "action": { + "id": "toolu_bdrk_01Jnaz6P19pZ13nHCJW5ZJ6K", + "name": "execute", + "input": { + "code": "await machine.exec({ command: \"python3 /tmp/dx.py ~/Desktop/Guidelines.docx > /tmp/guidelines.txt 2>&1; wc -l /tmp/guidelines.txt\" })\nvar gtext = await machine.readFile({ path: \"/tmp/guidelines.txt\" })\nvar lines = gtext.split(\"\\n\")\nconsole.log(\"TOTAL LINES:\", lines.length)\nconsole.log(lines.slice(0, 60).map((l,i)=>`${i}: ${l}`).join(\"\\n\"))", + "title": "Reading the first part of the Guidelines data" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_7_20260923@111841379223.png", + "osworld_action": "WAIT" + }, + { + "step_num": 8, + "action": { + "id": "toolu_bdrk_01VTEdRMhYJrxgSQNANButSM", + "name": "execute", + "input": { + "code": "console.log(lines.slice(59, 70).map((l,i)=>`${i+59}: ${l}`).join(\"\\n\"))\nconsole.log(\"\\n\\n===== PROPOSAL.DOCX (procedure guide) =====\")\nawait machine.exec({ command: \"python3 /tmp/dx.py ~/Desktop/proposal.docx > /tmp/proposal.txt 2>&1\" })\nvar ptext = await machine.readFile({ path: \"/tmp/proposal.txt\" })\nconsole.log(ptext)", + "title": "Reading the remaining sections and the procedure guide" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_8_20260923@111853323834.png", + "osworld_action": "WAIT" + }, + { + "step_num": 9, + "action": { + "id": "toolu_bdrk_01MpdgL4EKGUEpaGNqnjHWmv", + "name": "execute", + "input": { + "code": "console.log(lines.slice(0, 32).map((l,i)=>`${i}: ${l}`).join(\"\\n\"))", + "title": "Reading Guidelines data lines 0-32" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_9_20260923@111905736754.png", + "osworld_action": "WAIT" + }, + { + "step_num": 10, + "action": { + "id": "toolu_bdrk_01Xku2brDwAThRDFhAJwEqnS", + "name": "execute", + "input": { + "code": "console.log(lines.slice(31, 62).map((l,i)=>`${i+31}: ${l}`).join(\"\\n\"))", + "title": "Reading Guidelines data lines 31-62" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_10_20260923@111914735933.png", + "osworld_action": "WAIT" + }, + { + "step_num": 11, + "action": { + "id": "toolu_bdrk_01PKnGUodbXDfMH4LDo7u3fa", + "name": "execute", + "input": { + "code": "console.log(lines.slice(3, 18).map((l,i)=>`${i+3}: ${l}`).join(\"\\n\"))", + "title": "Reading project information section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_11_20260923@111924879854.png", + "osworld_action": "WAIT" + }, + { + "step_num": 12, + "action": { + "id": "toolu_bdrk_01GE1TySp2e7ZtZcFoLsKXp3", + "name": "execute", + "input": { + "code": "console.log(lines.slice(18, 32).map((l,i)=>`${i+18}: ${l}`).join(\"\\n\"))", + "title": "Reading proposal details and objectives" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_12_20260923@111935140056.png", + "osworld_action": "WAIT" + }, + { + "step_num": 13, + "action": { + "id": "toolu_bdrk_01KJ2h9fXKoLRZTRffiFNXjb", + "name": "execute", + "input": { + "code": "console.log(lines.slice(32, 50).map((l,i)=>`${i+32}: ${l}`).join(\"\\n\"))", + "title": "Reading the budget plan table" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_13_20260923@111945007488.png", + "osworld_action": "WAIT" + }, + { + "step_num": 14, + "action": { + "id": "toolu_bdrk_01AsNxwxcFx58tnFDiYwgyxz", + "name": "execute", + "input": { + "code": "console.log(lines.slice(50, 70).map((l,i)=>`${i+50}: ${l}`).join(\"\\n\"))", + "title": "Reading declarations and undertaking sections" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_14_20260923@111954058980.png", + "osworld_action": "WAIT" + }, + { + "step_num": 15, + "action": { + "id": "toolu_bdrk_01DbLhPG2Ls5BdvsCRmWFmTn", + "name": "execute", + "input": { + "code": "console.log(\"LINE 20 (Reasons for Resubmission):\")\nconsole.log(lines[20])", + "title": "Reading the full reasons for resubmission text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_15_20260923@112005248857.png", + "osworld_action": "WAIT" + }, + { + "step_num": 16, + "action": { + "id": "toolu_bdrk_01Te34FZcM2JaGXyzPPw4ksr", + "name": "execute", + "input": { + "code": "for (var i = 21; i <= 31; i++) { console.log(`--- ${i}: ${lines[i]}`) }", + "title": "Reading the project objectives section" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_16_20260923@112015824963.png", + "osworld_action": "WAIT" + }, + { + "step_num": 17, + "action": { + "id": "toolu_bdrk_01PkG8g3XMUyt7EoJR31Unb9", + "name": "execute", + "input": { + "code": "console.log(\"HEADER:\", lines[39])\nconsole.log(\"\\nROW1:\", lines[40])", + "title": "Reading the first budget item row" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_17_20260923@112025222642.png", + "osworld_action": "WAIT" + }, + { + "step_num": 18, + "action": { + "id": "toolu_bdrk_01H3ipwrUTHoZbTyMvexgZLP", + "name": "execute", + "input": { + "code": "console.log(\"ROW2:\", lines[41])", + "title": "Reading the second budget item row" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_18_20260923@112033962943.png", + "osworld_action": "WAIT" + }, + { + "step_num": 19, + "action": { + "id": "toolu_bdrk_0184u2mJ99wQdvoovsbyF6ym", + "name": "execute", + "input": { + "code": "for (var j = 42; j <= 48; j++) { console.log(`[${j}] ${lines[j]}`) }", + "title": "Reading the rest of the budget table" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_19_20260923@112042973647.png", + "osworld_action": "WAIT" + }, + { + "step_num": 20, + "action": { + "id": "toolu_bdrk_019LM9V2YvLcc5ViaHYPWMzi", + "name": "execute", + "input": { + "code": "console.log(\"LINE 49 (Justifications for Funding):\")\nconsole.log(lines[49])", + "title": "Reading the funding justification text" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_20_20260923@112053875867.png", + "osworld_action": "WAIT" + }, + { + "step_num": 21, + "action": { + "id": "toolu_bdrk_01TcVyQwbdzx88xAAbLrxgJj", + "name": "execute", + "input": { + "code": "var appData = {\n projectTitle: \"Enhancing Robust Multimodal Learning via Efficient Model Alignment and Adaptive Optimization\",\n applicationYear: \"2025-2026\",\n pi: { name: \"Cheung, Man Hon\", email: \"mansonmh@hku.hku\", dept: \"Research Services of the Registry\", role: \"Principal Investigator (PI)\", rcr: \"Yes\" },\n reasons: lines[20],\n objectives: [lines[23], lines[25], lines[27], lines[29]],\n currency: \"HKD\",\n budget: [\n { item: \"Equipment\", segment: \"19002\", amount: \"20000\", just: lines[40].split(\" | \")[3] },\n { item: \"General Expenses\", segment: \"19003\", amount: \"8000\", just: lines[41].split(\" | \")[3] },\n { item: \"Overseas Travelling\", segment: \"19010\", amount: \"10000\", just: lines[42].split(\" | \")[3] },\n { item: \"Staffing\", segment: \"29030\", amount: \"10000\", just: lines[43].split(\" | \")[3] },\n ],\n total: \"46000\",\n justifications: lines[49],\n undertaking: \"Yes\",\n additionalUndertaking: lines[63]\n}\nconsole.log(\"Stored. Budget items:\", appData.budget.map(b=>`${b.item}/${b.segment}/${b.amount}`).join(\", \"))\nvar pl = ptext.split(\"\\n\")\nconsole.log(\"\\n=== PROCEDURE lines 0-20 ===\")\nconsole.log(pl.slice(0,20).map((l,i)=>`${i}: ${l}`).join(\"\\n\"))", + "title": "Storing application data and reading the procedure steps" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_21_20260923@112111607426.png", + "osworld_action": "WAIT" + }, + { + "step_num": 22, + "action": { + "id": "toolu_bdrk_016wPipoAdRqJgJSeGhd8K5u", + "name": "execute", + "input": { + "code": "console.log(pl.slice(19,29).map((l,i)=>`${i+19}: ${l}`).join(\"\\n\"))", + "title": "Reading the remaining procedure steps" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_22_20260923@112120648030.png", + "osworld_action": "WAIT" + }, + { + "step_num": 23, + "action": { + "id": "toolu_bdrk_01SHnJsNP8MiztV6QzynLyVW", + "name": "execute", + "input": { + "code": "console.log(pl.slice(0,14).map((l,i)=>`${i}: ${l}`).join(\"\\n\"))", + "title": "Reading the opening procedure steps" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_23_20260923@112130960464.png", + "osworld_action": "WAIT" + }, + { + "step_num": 24, + "action": { + "id": "toolu_bdrk_01UX9cbiRYRj9WJAMhmp6Ghr", + "name": "execute", + "input": { + "code": "var gg = await machine.exec({ command: \"grep -in -E 'date|duration|month|cap|year' /tmp/guidelines.txt\" })\nconsole.log(gg.stdout || \"(no matches)\")\nconsole.log(\"---- windows ----\")\nvar wins2 = await machine.listAppWindows()\nwins2.forEach(w=>console.log(w.id, \"|\", w.title))", + "title": "Checking for date and duration fields" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_24_20260923@112148738303.png", + "osworld_action": "WAIT" + }, + { + "step_num": 25, + "action": { + "id": "toolu_bdrk_01F6pBQYyZKEDj8wtpE5EbGW", + "name": "execute", + "input": { + "code": "var chrome = await machine.getAppWindow(67)\nawait chrome.activate()\nawait wait({ waitTime: 1 })\nvar snap = await chrome.snapshot()\nconsole.log(snap.snapshot.outline())", + "title": "Opening the RIMS dashboard in Chrome" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_25_20260923@112210088280.png", + "osworld_action": "WAIT" + }, + { + "step_num": 26, + "action": { + "id": "toolu_bdrk_01UwSN5H4TJMNahDuX5R9qwj", + "name": "execute", + "input": { + "code": "await chrome.click({ ref: '148' })\nawait wait({ waitTime: 1 })\nawait chrome.click({ ref: '138' }) // + Add New Content\nawait wait({ waitTime: 1.5 })\nvar snap2 = await chrome.snapshot()\nconsole.log(snap2.snapshot.outline())", + "title": "Clicking Add New Content" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_26_20260923@112233162693.png", + "osworld_action": "WAIT" + }, + { + "step_num": 27, + "action": { + "id": "toolu_bdrk_017y8zHz7AihssXiL1PqZ94N", + "name": "execute", + "input": { + "code": "await chrome.click({ ref: '138' })\nawait wait({ waitTime: 1.5 })\nawait chrome.screenshot()", + "title": "Opening the Add New Content menu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_27_20260923@112249711409.png", + "osworld_action": "WAIT" + }, + { + "step_num": 28, + "action": { + "id": "toolu_bdrk_01XYpiiqXzKnxibQbWXe33ZZ", + "name": "execute", + "input": { + "code": "await chrome.click({ ref: '145' })\nawait wait({ waitTime: 2 })\nvar snap3 = await chrome.snapshot()\nconsole.log(snap3.snapshot.outline())", + "title": "Selecting Proposal from the menu" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_28_20260923@112308189719.png", + "osworld_action": "WAIT" + }, + { + "step_num": 29, + "action": { + "id": "toolu_bdrk_01Ac3FoWbbpsnTrx8b8h6tsf", + "name": "execute", + "input": { + "code": "chrome = await machine.getAppWindow(\"Google Chrome\")\nvar s = await chrome.snapshot()\nconsole.log(s.snapshot.grep(\"Seed Fund|Internal|Next|Cancel\", { context: 0 }))", + "title": "Reconnecting to Chrome and finding the proposal type" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_29_20260923@112332516718.png", + "osworld_action": "WAIT" + }, + { + "step_num": 30, + "action": { + "id": "toolu_bdrk_01AkWMj5sCcqgL3dFTq6vcGo", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1067, y: 241 }) // Cancel in the JS dialog\nawait wait({ waitTime: 1.5 })\nawait machine.click({ x: 851, y: 693 }) // \"Seed Fund for Basic Research for Resubmission of GRF/ECS Proposals\"\nawait wait({ waitTime: 1.5 })\nawait chrome.screenshot()", + "title": "Selecting Seed Fund for Basic Research for Resubmission" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_30_20260923@112351072008.png", + "osworld_action": "WAIT" + }, + { + "step_num": 31, + "action": { + "id": "toolu_bdrk_01LgorVfN3YaNnWeuxc7Hzvi", + "name": "execute", + "input": { + "code": "await machine.click({ x: 1157, y: 263 }) // OK\nawait wait({ waitTime: 2 })\nawait chrome.screenshot()", + "title": "Confirming the proposal type selection" + } + }, + "reward": 0, + "done": false, + "screenshot_file": "step_31_20260923@112405795724.png", + "osworld_action": "WAIT" + }, + { + "step_num": 32, + "action": { + "id": "toolu_bdrk_01Ur2uL6nVqRt62wTGtJR2MH", + "name": "execute", + "input": { + "code": "var h = await machine.exec({ command: \"wc -l ~/Desktop/HKU-RIMS-System/proposal-form.html && grep -n 'id=\\\"\\\\|]*id=\\\"[^\\\"]+\\\"' ~/Desktop/HKU-RIMS-System/proposal-form.html | sed -E 's/^([0-9]+):.*id=\\\"([^\\\"]+)\\\".*/\\\\1 -> \\\\2/'\" })\nconsole.log(h6.stdout)\nvar h7 = await machine.exec({ command: \"grep -n -E 'class=\\\"tab-btn|tab-content\\\" id=|

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