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license: apache-2.0
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https://huggingface.co/datasets/neomonde-lab/law-e-framework/resolve/main/README.md
# Law E Framework – Thermodynamic Governance for AI Reliability

**Short description**

Law E is an operational framework that treats modern AI systems as thermodynamic information processes.  
It introduces a native governance layer that observes the “energy cost” and coherence of model outputs, and uses this signal to regulate hallucinations and unstable behaviors.

This repository hosts the **initial technical report** describing the framework, its main equations and the first proof-of-concept design.

📄 **PDF**: [`Law_E_Framework.pdf`](./Law_E_Framework.pdf)

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## Why Law E?

- Large language models are powerful but prone to **hallucinations**.
- Current guardrails are mostly **symbolic or heuristic**.
- Law E proposes a **physics-inspired governance layer**:
  - monitors useless energy dissipation ΔE
  - tracks global organization / stability
  - regulates inference when the system drifts

The goal is to move toward **self-regulated, energy-aware AI systems**.

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## Current status

- Conceptual framework and equations defined.
- First regulator–selector POC in development.
- Next steps:
  - standardized hallucination evaluation (TruthfulQA, etc.)
  - CPU/energy proxy metrics
  - public demonstrator for selected models.

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## Contact & Collaboration

Created by **Sébastien Favre-Lecca (Neomundi Lab)**  

- Website: https://neomonde.io  
- Twitter: 

If you are working on AI safety, energy-aware AI, or robotics and want to collaborate on Law E evaluation or implementation, feel free to reach out.

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## Experimental Reports (Zenodo mirror)

This repository also hosts a set of experimental research reports archived on Zenodo,
documenting empirical observations and audit-level traces related to information–energy
dynamics and endogenous regulation in artificial systems (Loi E).

### ### Available experimental reports

- **Information–Energy Dynamics Simulator (Loi E) — Experimental Observations**  
  Systematic exploration of information–energy regimes in a minimal simulator, including
  non-uniform dissipation, distributed asymmetry, recoverability windows, and irreversibility thresholds.  
  📄 PDF: `Information-energy dynamics simulator Loi E — Experimental observations.pdf`  
  🔗 Zenodo DOI: https://doi.org/10.5281/zenodo.18483571

- **Regulator–Selector (Reg-Sel): Measured effects of endogenous regulation in large language models**  
  Experimental evaluation of a minimal inference-time regulation mechanism applied to LLMs,
  without modifying model weights, prompts, or using external supervision.  
  📄 PDF: `Measured effects of endogenous regulation in large language models.pdf`

- **Filtre E — Audit trace example: information–energy regulation**  
  Audit-level illustration of explicit information–energy thresholding and regulated versus
  non-regulated response regimes.  
  📄 PDF: `Filtre E — audit trace example information-energy regulation.pdf`

These documents form a coherent experimental foundation complementing the Law E framework.
Interpretative discussions (e.g. negentropy, biological analogy, governance implications) are
intentionally addressed in separate publications.

Further experimental reports and technical notes will be added to this repository as the Law E research progresses.