Omega Framework v1.0 — Standalone Governance Layer for Negentropic AI Systems
Steven Lanier-Egu · Zenodo (CERN European Organization for Nuclear Research) · 2025
Omega Framework v1.0 is a standalone, substrate-agnostic governance layer for complex AI systems. It provides a practical safety kernel (“Gatehouse”) that continuously measures system predictability, drift, coherence to declared objectives, insulation from adversaries (“veil”), and continuity of the acting observer identity. When risk rises or a leak is detected, Omega prioritizes containment and rollback, raises defenses, and can escalate to deeper shadow-simulation checks. The framework defines auditable metrics, a cost-aware mirroring model with three approximation tiers, green-route caching, and deterministic composition for policy packs. Thresholds ship as data-backed priors with provenance and a public refit workflow, encouraging domains to recalibrate rather than copy defaults. Inter-Omega protocols support multi-agent collaboration with conservation boundaries, ethics gating, quarantine, and transparent boundary accounting. Omega can operate entirely on its internal reference substrate, while offering normative drivers for optional integration with richer simulation stacks. The specification includes a telemetry schema, governance hooks, and minimal proof obligations for leak-first preemption, bounded time dilation, and export conservation. Authors and credit: Steven Lanier-Egu; SEAL (Systems for Emergent Alignment & Low-Entropy) Division.Version: 1.0 (General Availability)License: CC BY-SA 4.0 for the specification text; reference implementations under Apache-2.0.