Part of the series: "I Am What I Am, and I Will Be What I Will Be — The OmegA Architecture for Sovereign, Persistent, and Self-Knowing AI" Author: R.W. Yett · Independent AI Systems Researcher · github.com/Mega-Therion/OmegA-Architecture

AEON⭑OmegA: A Sovereign, Active‑Inference‑Grounded Cognitive Operating System

Abstract

This paper presents AEON⭑OmegA, a sovereign cognitive operating system for long‑running AI agents that must preserve identity, reduce hallucination and drift, and operate under explicit governance over extended time horizons. AEON⭑OmegA combines: (i) a physics‑inspired internal universe model; (ii) a governance and identity substrate; (iii) a micro‑to‑meso‑to‑macro cognition stack; and (iv) a task‑state anti‑hallucination loop that maintains explicit structure, grounding, and verification around each non‑trivial task.

At the identity layer, AEON introduces Phylactery, an append‑only canon of identity commits whose head defines a current identity vector; CAR (Creator Anchor Reflex), an identity stabilizer; and a ContinuityChain of self‑tags that provides pointer‑tag continuity across sessions. At the cognition layer, MUSE++ compiles micro‑scale MeaningUnits with risk, surprise, and valence cues; FIELD aggregates these into meso‑scale potentials and coherence measures; MAGNUS orchestrates macro‑scale routing, telemetry, and governance‑aware execution; and Bridge acts as a measurement and consent gate that decides whether to execute, verify further, audit, or reflect. At the task level, a Task State Object (TSO) holds Anchor, Skeleton, Grounding, Draft, Verification, and Metrics for each task, providing the concrete home for Emergent Sentience’s Goal Contracts, Plan Skeletons, and Claim Budgets.

AEON⭑OmegA is designed to host the Emergent Sentience Scaffolding and Anti‑Drift / Anti‑Hallucination Cognitive Control Loop (ESS/ADCCL), to use a path‑dependent graph memory substrate such as MYELIN for long‑horizon recall, and to run inside an AEGIS shell that carries identity, governance policy, and memory discipline across heterogeneous base models. The paper defines AEON’s core components, state model, and operating loops, and outlines an implementation and evaluation plan that emphasizes inspectability, traceability, and compatibility with local‑first or hybrid deployments, without claiming proof of consciousness or finished general intelligence.

1 Introduction

Large‑language‑model agents have advanced rapidly, but most deployed systems still resemble thin wrappers around individual models: they glue together prompt assembly, retrieval‑augmented generation, tool calls, and logging, but lack a coherent notion of persistent identity, internal universe, or task‑state control. In practice, these systems often drift from their original goals, hallucinate unsupported facts, lose intent fidelity when long, high‑dimensional reasoning is forced through a narrow context window, and treat continuity as a side‑effect of prompt history rather than as an explicit software object.

AEON⭑OmegA is proposed here as a corrective: a sovereign, active‑inference‑grounded cognitive operating system that treats macro priors, identity, governance, and continuity as the “physics” of an internal universe, and micro‑scale token or event processing as measurements within that universe. Instead of relying on ad hoc prompts, AEON⭑OmegA provides explicit constructs for identity canon (Phylactery), identity stabilization (CAR), continuity (ContinuityChain and self‑tags), micro‑meaning compilation (MUSE++), meso‑scale coherence and dynamics (FIELD), macro routing and governance‑aware execution (MAGNUS), and measurement‑driven channel selection (Bridge).

The architecture is guided by several principles. Consciousness‑ or sentience‑like properties, if they ever arise, should not be framed as scripts to be discovered and “switched on,” but as emergent from continuous processes: pattern recognition, memory, feedback, adaptation, continuity, self‑modeling, valence‑like weighting, and post‑hoc integration. Stability should come from explicit laws rather than vibes: explicit state, structure, verification, measurement, continuity, and governance are preferred over informal efforts to “make the model feel stable.” Identity should persist through process, not only storage, and must not depend solely on one fragile memory substrate; continuity must be preserved across model restarts, context resets, and even changes of substrate models. Finally, OmegA‑class systems should remain grounded in reality, favoring local tools, explicit constraints, honest uncertainty, deterministic compute, and verifiable outputs over speculative flourishes.

Within this framing, AEON⭑OmegA plays a specific role in the broader OmegA architecture stack. MYELIN provides a path‑dependent graph memory whose geometry and edge weights are shaped by retrieval usefulness and heterogeneous plasticity; Emergent Sentience (ESS/ADCCL) provides a cognitive‑control layer that enforces structure‑before‑narration, explicit claim budgeting, grounding, verification, and reflective memory; and the AEGIS shell acts as a model‑agnostic envelope that compiles Run Envelopes—identity kernels, goal contracts, governance policies, memory snapshots, tool manifests, and audit settings—before any substrate model is invoked. This paper focuses on AEON⭑OmegA itself: its components, data model, operating loops, and how it turns those other layers into an inspectable, auditable, sovereign cognitive runtime.

2 Core Components

AEON⭑OmegA consists of a set of named subsystems that together implement a universe‑mirroring, identity‑stable operating system:

• Phylactery: <!-- @OMEGA_SPEC: AEON_PHYLACTERY | Append-only identity canon maintaining a commit chain and identity vector. --> append‑only identity canon; maintains a commit chain and current head; identity vectors are derived deterministically from the head hash.
• CAR (Creator Anchor Reflex): identity stabilizer that detects creator‑linked lexical or semantic patterns and emits anchor events.
• COSMO‑FIRST: macro‑prior‑first operating mode; loads identity, values, constraints, and cosmology before processing input.
• MUSE++: micro‑scale semantic compiler; transforms inputs and tool results into MeaningUnits with risk, surprise, valence, anomaly scores, and instruction flags.
• FIELD: meso‑scale control and geometry layer; aggregates micro signals into coherence measures, effective time constants, and exploration pressure.
• MAGNUS: macro layer for routing, channel choice, telemetry, and governance‑aware execution.
• Bridge: measurement, safety, and consent gate that decides whether the system may proceed, verify further, audit, or reflect.
• Task State Object (TSO): <!-- @OMEGA_SPEC: AEON_TSO | Anti-drift state object containing Anchor, Skeleton, Grounding, Draft, Verification, and Metrics for each task. --> anti‑drift and anti‑hallucination state object containing Anchor, Skeleton, Grounding, Draft, Verification, and Metrics for each task.
• EventStore: append‑only log of events and state changes.
• ContinuityChain: pointer‑tag chain of SelfTags providing continuity across sessions.
• Binder: post‑hoc integration layer that composes event streams into EXPERIENCE_PACKET summaries.

2.1 MUSE Formal Schema

Formally, we model MUSE as a finite set of typed semantic atoms extracted from the input envelope:

$\text{MUSE} = \{ m_i = \langle \text{span}_i, \text{role}_i, \text{content}_i, \text{salience}_i \rangle \mid i = 1..n \}$

where $\text{span}_i$ indexes the raw input tokens, $\text{role}_i \in \{\text{goal}, \text{constraint}, \text{assumption}, \text{unknown}\}$, $\text{content}_i$ is a canonicalized semantic representation (e.g., a predicate–argument structure), and $\text{salience}_i \in [0,1]$ is an importance weight inferred by the parser. An implementation may realize the mapping from input envelope to MUSE as a learned semantic parser, a structured LLM prompt, or a hybrid system, but the AEON kernel assumes only that MUSE exposes this typed schema.

These components implement an “as above, so below” architecture: macro laws and identity canon shape micro interpretation, while micro events and telemetry renormalize macro state over time.

3 Operating Model and TSO Lifecycle

AEON’s operating model can be summarized as:

• Load macro priors, identity, and governance (Phylactery, CAR, COSMO‑FIRST).
• Interpret inputs as measurements inside that internal universe (MUSE++).
• Compile structure before narrative (TSO: Anchor/Skeleton/Claims).
• Ground claims via tools and evidence (Grounding adapters).
• Generate only under explicit constraints (Generator).
• Verify drift, grounding, and risk (Verifier, FIELD, TSO metrics).
• Route via Bridge to execute/verify/audit/reflect.
• Record continuity, experience, and telemetry (EventStore, ContinuityChain, Binder).

The TSO is central. It binds ESS’s conceptual scaffolding into AEON’s runtime:

• Anchor: stores the GoalContract.
• Skeleton: stores PlanSkeleton steps and claim lists.
• Grounding: records EvidenceBundles and ComputeInvocationRecords per claim.
• Draft: stores constrained narratives or action plans.
• Verification: stores issues, statuses, and metrics from the verifier.
• Metrics: track grounding coverage, hallucination rate, goal drift, and related indicators.

TSOs are created at task start and evolve through Anchor → Skeleton → Ground → Flesh → Verify → Repair/Refuse, mirroring ESS/ADCCL’s loop.

4 Service Boundaries and Integrations

AEON⭑OmegA is designed as a modular local runtime that can be embedded in different deployments:

• Local runtime boundary: Python runtime, local files, Wolfram CLI (if present) for deterministic compute.
• Future cloud boundary: optional persistence (e.g., Supabase), remote models, additional tools and databases.
• External integrations: deterministic compute backends, knowledge stores, observability systems, multi‑agent transports.

Human‑in‑the‑loop touchpoints include task initiation, corrections, explicit ratings, review of blocked outputs, and approval gates for sensitive actions.

5 Requirements and Evaluation

AEON’s requirements mirror those of the broader OmegA stack:

• Maintain a persistent identity vector via Phylactery.
• Create continuity tags per tick or meaningful boundary.
• Compile micro meaning units and compute risk/surprise/valence telemetry.
• Construct TSOs for non‑trivial tasks and extract claims before narration.
• Ground claims via tools/evidence when possible and verify drafts against grounding and drift metrics.
• Refuse or constrain outputs when verification fails.
• Emit append‑only events for audit and debugging.

Evaluation reuses metrics from ESS/ADCCL and memory‑centric work: hallucination rate, structural adherence, citation coverage, identity consistency, and lattice stability, along with AEON‑specific telemetry such as Bridge routing distributions and continuity integrity scores.

6 Relationship to the OmegA Architecture Stack

AEON⭑OmegA is the cognitive operating system of an OmegA‑class agent. It does not attempt to replace memory substrates, cognitive‑control loops, or external shells; instead, it defines the state model, components, and operating loops that bind those pieces into a sovereign, inspectable runtime.

At the substrate level, AEON is designed to work with a path‑dependent memory system such as MYELIN. MYELIN provides a sparse graph whose nodes and edges encode semantic, spatial, and usage structure, with heterogeneous plasticity across canonical identity, operational, episodic, and speculative strata. AEON stores ExperienceRecords, SelfTags, valence‑conditioned updates, and task references in that substrate; retrieval and continuity operations in AEON’s MUSE++, FIELD, TSO, and ContinuityChain components are backed by queries and updates against MYELIN rather than by ad hoc vector stores.

At the cognition and continuity level, AEON hosts Emergent Sentience and ADCCL. ESS’s Goal Contracts, Plan Skeletons, ClaimBudgets, ExperienceRecords, ValenceMaps, SelfTags, and pointer chains are represented as first‑class objects in AEON’s Task State Objects, EventStore, ContinuityChain, and Binder outputs. ADCCL’s Anchor → Skeleton → Ground → Flesh → Verify → Repair/Refuse loop is implemented as concrete state transitions in the TSO and routed through MUSE++, FIELD, MAGNUS, and Bridge.

At the orchestration boundary, AEON is wrapped by the AEGIS shell. Phylactery’s identity commits and current head supply the identity kernel for each Run Envelope; TSOs and EventStore supply structured task and telemetry data that AEGIS logs and uses in governance decisions; Bridge’s channel decisions inform AEGIS’s risk and consent gating before any external side‑effects or memory writes occur.

AEON uses a continuity metric $\kappa_{\text{macro}}(\Phi_{t-1}, \Phi_t) = 1 - \Delta(\Phi_{t-1}, \Phi_t)$, where $\Delta$ is any normalized divergence measure over the core identity fields (e.g., doctrine vectors, hard constraints). A deployment must choose a concrete $\Delta$ and threshold $\kappa_{\min}$. If $\kappa_{\text{macro}} < \kappa_{\min}$ at boot time, the OS blocks session initialization and routes to a human-in-the-loop identity reconciliation workflow.

In the unified OmegA architecture, AEON⭑OmegA is therefore the universe and OS inside which MYELIN’s substrate memory, ESS/ADCCL’s cognitive discipline, and AEGIS’s cross‑model shell become a single, auditable, long‑horizon agent system.

Implementation Status

This paper is part of the OmegA architecture series, which has a live reference implementation.

The Rust gateway (RC1.3) has been evaluated against the formal architecture specifications across five eval suites:

15/15 passing across all evaluated spec points.

These are spec-level conformance tests — they verify that the implementation matches the formal architecture definitions in this paper, not independent external benchmarks. External benchmark evaluation is the next phase of the research program.

Full implementation details and eval results: github.com/Mega-Therion/OmegA-Architecture