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April 16, 20260 citationsOpen Access

ENTRO-OMEGA: Grand Unification of Informational Entropy — OMEGA v1.0 Universal Adaptive Stabiliser for Autonomous AI Infrastructure

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SBSamir Baladi

Key Points

  • The aim is to unify previous entropic protocols into a single adaptive system to enhance autonomous AI infrastructure.
  • Developed OMEGA v1.0 integrating nine previous protocols into a portable system.
  • Formulated Omega State Function Ωcore(t) using non-equilibrium statistical mechanics.
  • Conducted empirical evaluations using three synthetic benchmarks: OmniScrape, PolyLLM, and HybridEdge.
  • Achieved a collapse rate of 0.0% across trials.
  • Obtained a mean peak-load reduction of 91.4% and a throughput gain of 44.5%.
  • Convergence index reached 0.9987, ensuring stability under bounded disturbances.

Abstract

ENTRO-OMEGA (E-LAB-10) is the tenth and culminating project of the EntropyLab decadal research programme. This paper presents OMEGA v1.0, a unified self-governing entropic control engine that synthesises all nine predecessor protocols (E-LAB-01 through E-LAB-09) into a single portable system deployable across cloud, edge, and local infrastructure without manual reconfiguration.The central theoretical contribution is the Omega State Function Ωcore(t) — a five-component dynamical vector simultaneously tracking thermodynamic dissipation (ρ), rhythmic pulsation (P), spectral memory fidelity (G), probabilistic collapse likelihood (Q), and network coherence (N) — governed by a Unified Field Equation (UFE) derived from non-equilibrium statistical mechanics and information geometry. The Universal Adaptive Stabiliser (UAS) is derived analytically from the UFE with formal input-to-state stability (ISS) guarantees, establishing that Ωcore(t) remains within a computable neighbourhood of the Absolute Steady State Ω* under all bounded disturbances. A self-adaptive breathing threshold θ(t), modulated by the composite Network Health Index H(t), eliminates the need for operator intervention across all tested stress regimes.Empirical evaluation across three synthetic benchmarks — OmniScrape (32-node scraper/ETL), PolyLLM (16-node LLM inference), and HybridEdge (64-node edge-cloud hybrid) — confirms: collapse rate 0.0%, mean peak-load reduction 91.4%, throughput gain 44.5%, convergence index Ωconv = 0.9987, and health steady state Hss > 0.991 across 900 total trials. All results meet or exceed pre-specified target values and surpass the best single-protocol baseline (ENTRO-PULSE, E-LAB-09) by synergistic integration rather than additive combination.ENTRO-OMEGA constitutes the first operational implementation of a Grand Unification Theory (GUT) for informational entropy, establishing a physical constitution for the next generation of autonomous AI infrastructure.EntropyLab Series: E-LAB-10 | OSF Registration: osf.io/6v4xt | Landing: entro-omega.netlify.app/ | MIT License © 2026 Samir Baladi

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Cite This Study

Samir Baladi (2026) studied this question.

synapsesocial.com/papers/69e07d732f7e8953b7cbe633https://doi.org/10.5281/zenodo.19562998
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