A Time-Free, Entropy-Driven Architecture for Emergent Intelligence
Michael Zot (ORCID: 0009-0001-9194-938X)
Contact: mike@stonetekdesign.com
The Concrescence Stack is a five-layer framework for adaptive, privacy-preserving, and culturally fair AI systems that operate without synchronized clocks or global state. The system detects emergent intelligence using event-driven signals, not time, and introduces a rigorous methodology for distributed AI resilience, fairness, and human alignment.
- Noëtic Events: Detect micro-intelligence via joint statistical triggers (entropy, uncertainty drop, and latent function shift).
- Time-Free Computation: Replaces temporal sequencing with structural (causal) precedence; works even if system clocks disagree.
- Federated Normalization: Promotes cultural fairness without imposing a global standard—adapts to local variation.
- Entropy Tokens: Aligns incentives by measuring and rewarding unpredictability and adaptability.
- Percolation Collapse Detection: Monitors network/system health and predicts phase transitions using the Event-Gravity Index.
- 203% increase in adaptive event detection vs. PPO-Clip baselines (p < 10⁻¹¹, Cohen's d = 2.8)
- 38% faster convergence using structural ordering vs. temporal ordering
- Robust gaming resistance: 0.06% bot false positive rate (95% CI: [0.04%, 0.09%])
- 95.7% precision in predicting network failure/collapse (10,000 simulations)
- 73% reduction in cross-cultural entropy variance (Gini coefficient) with federated normalization
- No independent replication by external groups yet
- No real-world deployment or production test so far
- All results from author simulations
- Not yet peer-reviewed
- All code and data are public for validation
Bottom line: This is a mathematically rigorous, testable scientific advance. Independent replication and real-world deployment are strongly encouraged.
git clone https://github.com/mikecreation/concrescence-stack.git
cd concrescence-stack
2. Install Dependencies
Requires Python 3.8+
Install via pip:
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pip install numpy networkx scipy
3. Run the Demo Experiment
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python noetic_stack_demo.py
This will run the percolation/EGI experiment and print correlation stats.
4. View/Compile the Manuscript
LaTeX file: concrescence_stack.tex
Compile using pdflatex concrescence_stack.tex (requires a TeX distribution such as TeX Live or MiKTeX)
File Overview
concrescence_stack.tex — Full manuscript (LaTeX)
noetic_stack_demo.py — Core reproducibility/demo script
LICENSE — Open source license
README.md — This file
Falsifiability & Replication
This work can be falsified if:
Random agents exceed a 5% noëtic event rate after thresholding
Percolation collapse is not predicted ≥90% of the time
Bot attacks pass at a rate >1% of humans
Noëtic events do not correlate with performance improvement
Replication is encouraged—please open an issue or PR for any failure cases or improvements.
Citation
If you use or build on this work, please cite:
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Michael Zot, "The Concrescence Stack: A Time-Free, Entropy-Driven Architecture for Emergent Intelligence", 2025.
GitHub: https://github.com/mikecreation/concrescence-stack
Contact
Author: Michael Zot
Email: mike@stonetekdesign.com
ORCID: 0009-0001-9194-938X
“We invite the research community to challenge, replicate, and extend this work.”