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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 |
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# Law E Framework – Thermodynamic Governance for AI Reliability |
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**Short description** |
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Law E is an operational framework that treats modern AI systems as thermodynamic information processes. |
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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. |
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This repository hosts the **initial technical report** describing the framework, its main equations and the first proof-of-concept design. |
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📄 **PDF**: [`Law_E_Framework.pdf`](./Law_E_Framework.pdf) |
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--- |
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## Why Law E? |
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- Large language models are powerful but prone to **hallucinations**. |
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- Current guardrails are mostly **symbolic or heuristic**. |
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- Law E proposes a **physics-inspired governance layer**: |
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- monitors useless energy dissipation ΔE |
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- tracks global organization / stability |
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- regulates inference when the system drifts |
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The goal is to move toward **self-regulated, energy-aware AI systems**. |
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## Current status |
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- Conceptual framework and equations defined. |
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- First regulator–selector POC in development. |
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- Next steps: |
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- standardized hallucination evaluation (TruthfulQA, etc.) |
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- CPU/energy proxy metrics |
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- public demonstrator for selected models. |
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## Contact & Collaboration |
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Created by **Sébastien Favre-Lecca (Neomundi Lab)** |
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- Website: https://neomonde.io |
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- Twitter: |
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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) |
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This repository also hosts a set of experimental research reports archived on Zenodo, |
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documenting empirical observations and audit-level traces related to information–energy |
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dynamics and endogenous regulation in artificial systems (Loi E). |
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### ### Available experimental reports |
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- **Information–Energy Dynamics Simulator (Loi E) — Experimental Observations** |
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Systematic exploration of information–energy regimes in a minimal simulator, including |
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non-uniform dissipation, distributed asymmetry, recoverability windows, and irreversibility thresholds. |
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📄 PDF: `Information-energy dynamics simulator Loi E — Experimental observations.pdf` |
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🔗 Zenodo DOI: https://doi.org/10.5281/zenodo.18483571 |
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- **Regulator–Selector (Reg-Sel): Measured effects of endogenous regulation in large language models** |
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Experimental evaluation of a minimal inference-time regulation mechanism applied to LLMs, |
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without modifying model weights, prompts, or using external supervision. |
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📄 PDF: `Measured effects of endogenous regulation in large language models.pdf` |
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- **Filtre E — Audit trace example: information–energy regulation** |
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Audit-level illustration of explicit information–energy thresholding and regulated versus |
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non-regulated response regimes. |
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📄 PDF: `Filtre E — audit trace example information-energy regulation.pdf` |
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These documents form a coherent experimental foundation complementing the Law E framework. |
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Interpretative discussions (e.g. negentropy, biological analogy, governance implications) are |
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intentionally addressed in separate publications. |
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Further experimental reports and technical notes will be added to this repository as the Law E research progresses. |
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