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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
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.
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.
Contact & Collaboration
Created by Sébastien Favre-Lecca (Neomonde Lab)
- Website: https://neomonde.tech
- Twitter: @GoldOracle_E
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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