Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Data Paper . 2026
License: CC BY
Data sources: Datacite
ZENODO
Data Paper . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Information–Energy dynamics and endogenous regulation in Artificial Systems (Loi E)

Authors: FAVRE-LECCA, Sébastien;

Information–Energy dynamics and endogenous regulation in Artificial Systems (Loi E)

Abstract

This repository contains three complementary experimental reports exploring the emergence and regulation of information–energy dynamics in artificial systems, within the framework of Loi E (Information–Energy Law). The work is entirely empirical and observational. It does not rely on learning, optimization, supervision, external grounding, or goal-driven mechanisms. All reported effects emerge from minimal algorithmic rules and explicit information–energy accounting. The three reports are: Information-Energy Dynamics Simulator (Loi E) — Experimental ObservationsA systematic exploration of information–energy regimes in a minimal simulator.The document reports observations across multiple experimental questions (Q1–Q10), including non-uniform dissipation, regime recurrence, distributed asymmetry, recoverability windows, and irreversibility thresholds.Detailed observations are provided in annexes, together with selected audit-level traces. Regulator–Selector (Reg-Sel): Minimal Endogenous Regulation for Large Language ModelsAn experimental study of a lightweight, inference-time regulation mechanism applied to large language models.Reg-Sel operates without modifying model weights, prompt engineering, or external supervision.Results show consistent improvements in response stability and truthfulness, with bounded computational overhead, across multiple architectures. Filtre E — Audit-Level Information-Energy RegulationA low-level regulation mechanism based on explicit information–energy thresholds.Filtre E governs response generation by constraining cumulative informational cost, producing more stable and bounded outputs under uncertainty.The report documents audit traces illustrating regulated versus non-regulated regimes. Together, these documents establish a coherent experimental foundation for studying information–energy dynamics and endogenous regulation in artificial systems.Interpretative discussions (e.g. negentropy, biological analogy, governance implications) are intentionally separated from these reports and addressed in subsequent works. The experiments were conducted by the author.An experimental user interface is available to the scientific community upon request for independent exploration and replication.

Keywords

Artificial intelligence, Thermodynamics, Thermodynamic engineering

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!