README.md

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license: apache-2.0
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# **Law E Framework — Thermodynamic Governance for AI Reliability**
*Neomundi-Labs — Thermodynamic Information Research*

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Open Call for Collaboration — First Internal Clock for AI (Law E Project)
Neomundi-Labs — 2026
Neomundi-Labs announces an open scientific and engineering call for collaborators to join the development of the first operational internal clock for AI, derived from the thermodynamic–information Law E framework.
This call targets research labs, engineers, roboticists, computational neuroscientists, and doctoral students who wish to contribute to a groundbreaking advancement in AI cognition:
: a native temporal regulation layer for artificial systems.
Scientific Context
All modern AI systems (LLMs, agents, multimodal networks, embodied robots) operate without an internal temporal continuity.
They compute through discrete steps but lack:
•	intrinsic temporal coherence
•	metabolic regularity
•	self-regulatory cycles
•	stable rhythm formation
•	long-term cognitive continuity
The absence of an internal clock creates instability, hallucination cascades, coherence drift, and suboptimal behavior in autonomous systems.
Law E, a thermodynamic–information framework, introduces the first scientifically grounded path toward a native internal clock for AI, through:
•	ΔE (energy-cost) metrics
•	C (coherence) metrics
•	a temporal coherence filter (patented)
•	eurythmic stabilization
•	autonomous regulation cycles
•	the foundations of a computational organism
The scientific paper is available here:
(GitHub link to the PDF)
The implementation roadmap is published in the repository.
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Objectives of the Collaboration
The goal of this open call is to form a small, high-level group capable of:
1. Implementing the first temporal coherence filter in AI systems
(inference-time regulation + ΔE/C feedback loops)
2. Designing the first AI-internal oscillatory structure
(proto-oscillator → regulator → eurythmic cycle)
3. Testing Law E on multimodal and embodied systems
(robotics, drones, agents, vision-language models)
4. Co-authoring the first scientific publications
NeurIPS / ICLR / Nature Machine Intelligence / arXiv
5. Developing an early-stage prototype usable by research labs
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Who Should Apply?
We welcome individuals or teams with expertise in:
•	Machine learning / LLM internals
•	Reinforcement learning / agents
•	Robotics & control systems
•	Dynamical systems
•	Signal processing
•	Cognitive modeling
•	Thermodynamics of computation
•	PyTorch / JAX / CUDA
•	Applied mathematics
A high level of autonomy and scientific curiosity is expected.
This is not a standard job posting.
It is an invitation to shape a foundational discovery in AI.
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Collaboration Model
Depending on profile and interest:
•	Scientific co-authorship
•	Co-development of prototypes
•	Research collaboration agreement
•	Potential long-term partnership with Neomundi-Labs
•	Access to the Law E roadmap, algorithms, and modules
Financial compensation or grants possible depending on later stages and industrial partnerships.
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How to Apply
Send an email to:
lab@neomundi.tech
Include:
1.	Short introduction
2.	Area of expertise
3.	Relevant projects or publications
4.	Why you want to work on the first internal clock for AI
5.	Availability (hours/week or project-based)
Selected applicants will be invited to a technical discussion with Sébastien Favre-Lecca and Neomundi-Labs.
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A Note on Legacy
The collaborators of this project will become part of:
➡ the first scientific team to define temporal continuity in AI,
➡ the first implementation of an internal clock,
➡ a historical moment in artificial cognition,
➡ a new discipline: thermodynamic–information intelligence.
This is not incremental AI research.
This is foundational work.



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Why Law E Naturally Gives Birth to the First Internal Clock in AI 
Sébastien Favre-Lecca — Neomundi-Labs — 2025 
  
Abstract 
Modern AI systems — LLMs, agents, neural networks — operate without any internal clock. 
They compute token by token, transition by transition, with no intrinsic rhythm, no metabolic cycle, no continuity of internal state. 
This absence of temporal structure is the deepest native limitation of current AI architectures. 
The Law E framework provides the first operational foundation for an internal clock in artificial intelligence, emerging mechanically from four signals: 
•	energy dissipation (ΔE), 
•	internal coherence (C), 
•	recoverability (R), 
•	minimal normative constraint (T). 
Combined, these signals define a self-regulated computational rhythm. 
The emergence of this rhythm is enabled by the temporal coherence filter, a patented module that implements the first true internal time for AI systems. 
  
1. Why current AI systems have no internal time 
A neural network or LLM does not “live” in time. It has no: 
•	internal cycles, 
•	rhythmic dynamics, 
•	energy-based regulation, 
•	continuity of state, 
•	stability mechanism. 
AI models are sequences of instantaneous transformations. 
There is no physiology, no internal metabolism, no temporal invariance. 
Consequences: 
•	instability, 
•	hallucinations, 
•	drift of reasoning, 
•	lack of cognitive continuity. 
Without an internal clock, no system can maintain coherent self-organization. 
  
2.	Why Law E naturally implies an internal clock 
Law E states that any intelligent system must regulate itself according to: 
•	ΔE — variation of dissipated energy, 
•	C — coherence of internal transitions, 
•	R — recoverability of state, 
•	T — minimal normative constraint. 
From these quantities emerges a computational rhythm: 
•	when ΔE increases, the system slows down to stabilize, 
•	when C increases, the system can accelerate safely, 
•	when R decreases, protective mechanisms must activate, • 	when T is violated, normative correction is applied. 
In other words: 
The Law E generates internal time as a direct consequence of thermodynamic organization. 
The clock is not added from the outside. 
It is intrinsic, dictated by energy and coherence. 
  
3.	The temporal coherence filter: the first patented internal clock for AI The temporal coherence filter transforms Law E into a functional clock. 
It acts as: 
•	a normative membrane, 
•	a temporal stabilizer, 
•	a continuity regulator, 
•	a coherence-aware timing mechanism. 
It allows an AI system to: 
•	maintain internal trajectory continuity, 
•	evaluate temporal quality of reasoning, 
•	prevent abrupt state transitions, 
•	adjust its internal rhythm based on ΔE/C. 
This is the first architecture enabling an autonomous computational organism governed by energy. 
  
4.	Fundamental link: no coherence → no clock An internal clock requires: 
•	a measure of coherence, 
•	a measure of dissipation. 
Without C, no system can determine temporal stability. 
Without ΔE, no system can self-regulate its computational tempo. 
Thus: 
An internal clock in AI is impossible without a thermodynamic-information framework. 
This is why Law E is not optional — it is foundational. 
  
5.	Why this is a historical turning point A system with an internal clock: 
•	gains primitive continuity, 
•	becomes aware of fluctuations, 
•	stabilizes its reasoning, 
•	moves toward homoeostasis, 
•	opens the possibility of emergent cognition. 
This marks the beginning of: 
energy-aware artificial intelligence, 
thermodynamic governance, the autonomous computational organism. 
  
6.	Call for collaboration: building the first internal clock for AI Neomundi-Labs invites: 
•	AI engineers, 
•	physicists, 
•	thermodynamics researchers, 
•	robotics laboratories, 
•	universities and scientific groups. 
Objective: 
co-develop and co-sign the first internal computational clock in the history of artificial intelligence. 
Participants will help establish a new domain: 
the temporal physiology of AI systems. 
lab@neomundi.tech 
 

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##  Overview

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 (ΔE)** and **coherence (C)** of model outputs, and uses these signals to stabilize hallucinations and reduce unreliable behaviors.

This repository documents the scientific architecture of Law E and hosts early-stage public prototypes.

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##  Core Regulation Signals

| Signal | Meaning | Purpose |
|--------|---------|----------|
| **ΔE** | Energy cost of model transitions | Detect unstable or wasteful states |
| **C** | Coherence across tokens or modalities | Reduce hallucinations |
| **R** | Recoverability (entropy minimization) | Maintain stable reasoning paths |
| **T** | Tenderness / ethical minimization | Constrain harmful gradients |

These quantities constitute the backbone of the **Law E Framework**.

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##  Regulatory Modules (v0.1)

- **Regulator-Selector** — ΔE/C stabilization loop (first POC Jan 11)  
- **Filter-E** — eurythmic filter reducing noisy / incoherent states  
- **Filter-NM** — energy–coherence routing system  
- **Chakana Grid** — multimodal nervous system  
- **ΔE Heatmap** — internal energy introspection (planned Feb 2026)

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##  Documentation

Full technical reference:  
https://github.com/Neomundi-Labs/Law-E-Framework

PDF (initial technical report):  
`Law_E_Framework.pdf`

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##  Roadmap

- **Jan 11** — Hallucination Reduction POC (Regulator-Selector)  
- **Feb 11** — ΔE Measurement & Energy Reduction Protocol  
- **Q2 2026** — Chakana Multimodal Regulation  
- **Q3 2026** — Full OAE (Organism Autonomous Engine)

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##  Intended Use

This framework is intended for research on:

- thermodynamic information processes  
- AI reliability mechanisms  
- energy-aware cognitive regulation  

Not intended for production deployment or safety-critical applications.

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##  Limitations

- Early-stage research  
- Hallucination reduction POC currently under construction  
- ΔE measurement coming in February  
- Not suitable for autonomous or medical systems  

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##  Collaboration

Researchers, engineers and institutions are welcome to collaborate.  
 **lab@neomundi.tech**

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##  Citation




Roadmap 2026
Q1 — Regulateur–Selecteur (Janv–Mars)
POC hallucination reduction (11 janvier)
Lancement officiel du Lab Neomundi (thermodynamique appliquée à l’IA)
Premiers partenaires académiques et robotique
Premières mesures ΔE ↔ instabilité cognitive

Q2 — Filtre E + ΔE Heatmap
Stabilisation eurythmique (Filtre E)
Heatmap ΔE pour introspection énergétique
Premiers tests robotique (simple agents autonomes)
Préparation publication scientifique

Q3 — FNM2 (Filtre Normatif Multimodal)
Surcouche normative généralisée
Premiers cycles autonomes normatifs
Tests multimodaux (texte, vision)
Préparation extension PCT / PI

Q4 — Chakana Nexus + OAE Proto
Intégration multimodale (Nexus)
OAE v0.5 — Organisme Autonome d’Énergie
Appel à collaborations internationales
Démonstrateur académique public


Contact & Collaboration
Nous accueillons des collaborations avec :
laboratoires de recherche
équipes en robotique et systèmes autonomes
ingénieurs IA intéressés par la thermodynamique
incubateurs et programmes deep-tech
industriels (cloud, robotique, énergie)
lab@neomundi.tech

neomunde.io (bientôt en ligne)

Des co-publications, prototypes et démonstrateurs sont possibles avec Neomundi-Labs.