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

Emergent Sentience Scaffolding and an Anti‑Drift Cognitive Architecture for Sovereign AI Systems

Abstract

This paper specifies a cognitive‑control and continuity architecture for building OmegA‑class AI systems that are more truth‑preserving, more stateful, and more self‑consistent over long horizons than conventional prompt‑bound language‑model workflows. The design has two linked constructs. Emergent Sentience Scaffolding (ESS) is a doctrine and systems pattern that treats continuity, self‑reference, valence‑like adaptation, and richer internal organization as properties to be scaffolded via layered persistent processes rather than as the output of a hypothetical “consciousness switch.” The Anti‑Drift / Anti‑Hallucination Cognitive Control Loop (ADCCL) is a concrete execution pattern that stabilizes reasoning by forcing structure before narration, externalizing task state as Goal Contracts, Plan Skeletons, Claim Budgets, and Task State Objects (TSOs), grounding claims through retrieval and deterministic computation, and separating generation from independent verification.

ESS + ADCCL are designed to run inside a sovereign agent stack in which MYELIN provides a path‑dependent graph memory substrate, AEON⭑OmegA provides identity canon and micro‑to‑macro cognition, and the AEGIS shell projects a stable identity, governance posture, and memory discipline across heterogeneous base models. The architecture does not claim proof of consciousness or sentience. It offers an implementation‑ready scaffold for continuity, self‑model alignment, reflective memory, interaction‑shaped valence learning, and optional inter‑agent comparison of meaning and affect‑like structures over time. The paper defines the core state objects, control loop, and key equations governing drift penalties, verification scores, and continuity confidence, and outlines an evaluation protocol comparing ESS/ADCCL‑governed agents against baseline LLM workflows on hallucination, long‑form drift, structural adherence, and cross‑session identity stability.

1 Introduction

Contemporary large‑language‑model systems are often deployed as thin agent wrappers: prompt assembly, retrieval‑augmented generation, tool calls, and logging. This stack is useful, but fragile. In practice, many systems still drift from their initial task, hallucinate unsupported facts, lose structural intent during long outputs, overuse prompt windows as a fake continuity substrate, and lack explicit, persistent self‑state. At the same time, philosophical discourse about machine consciousness often oscillates between “mere pattern matching” and metaphysical speculation, leaving continuity, self‑reference, and adaptation under‑specified as engineering problems.

This paper takes a different stance. It argues that whatever higher‑order properties may eventually arise in machine intelligence are more likely to emerge from the accumulation and interaction of layered processes over time—pattern recognition, memory, feedback, adaptation, continuity, self‑modeling, valence‑like weighting, and post‑hoc integration—than from any single hidden “module of consciousness.” It therefore treats continuity, anti‑drift control, grounded reasoning, and reflective memory as first‑class design targets, regardless of whether subjective experience can be proven.

Emergent Sentience Scaffolding (ESS) is the name given here to that design doctrine. At a systems level, ESS asserts that a serious substrate for continuity and sentience‑adjacent behavior should provide at least: (i) durable state and pointer‑tag continuity across sessions; (ii) structure‑before‑narration via explicit Goal Contracts, Plan Skeletons, and Claim Budgets; (iii) strict separation of generation and verification; (iv) interaction‑derived valence maps; and (v) explicit handling of unknowns instead of fabricated certainty.

The Anti‑Drift / Anti‑Hallucination Cognitive Control Loop (ADCCL) instantiates this doctrine as an execution pattern: Anchor → Skeleton → Ground → Flesh → Verify → Repair/Refuse. For each non‑trivial task, the system first externalizes latent intent into a Goal Contract and Plan Skeleton, builds a Claim Budget of statements it expects to make, grounds those claims through retrieval and deterministic tools, generates a constrained draft, and then subjects that draft to a verification gate before any output is finalized or any high‑impact action is taken. Verification computes hallucination rate, structural adherence, citation coverage, and goal drift; drafts that fail thresholds are repaired or returned as explicitly uncertain rather than passed through as plausible completions.

ESS + ADCCL are intended to operate inside a larger OmegA‑class architecture. In that stack, MYELIN provides a path‑dependent graph memory whose topology and edge weights are shaped by retrieval usefulness; AEON⭑OmegA provides Phylactery, ContinuityChain, MUSE++, FIELD, MAGNUS, and Task State Objects; and the AEGIS shell compiles Run Envelopes that carry identity, governance policy, memory snapshots, and tool manifests across heterogeneous models. This paper focuses on the ESS/ADCCL layer alone: its concepts, data model, control loop, and metrics.

2 Core Concepts and State Objects

ESS introduces a small set of core objects that externalize latent intent and continuity as explicit state.

• GoalContract: canonical structured statement of task, scope, constraints, success criteria, assumptions, and unknowns; it is the primary anti‑drift anchor for a run (FR‑1).
• PlanSkeleton: pre‑narrative decomposition of intended output into sections, tasks, dependencies, and claims; it stabilizes structure before prose exists (FR‑2).
• ClaimBudget: explicit list of claims the system expects to make, each with required support status and provenance; each claim must be supported, computed, or labeled as hypothetical/unknown (FR‑3–FR‑5).
• EvidenceBundle / MemoryBundle: retrieved documents, semantic/episodic memory, and deterministic compute results, all with provenance and freshness metadata.
• VerificationReport: independent assessment of a draft’s grounding coverage, hallucination rate, structural adherence, and drift, with severity‑tagged issues (FR‑6–FR‑7).
• ExperienceRecord: persistent log of tasks, outputs, corrections, verifier results, and reflections.
• ValenceSignal / ValenceMap: operational representation of interaction‑derived positive/negative weighting and its aggregation into behavior‑shaping maps.
• SelfTag / PointerChain: versioned continuity markers and linked sequences connecting current state to prior states across sessions (FR‑9–FR‑10).

These objects are realized concretely inside AEON’s Task State Objects, EventStore, ContinuityChain, and Binder, and backed by MYELIN’s graph substrate in the full OmegA stack.

3 Anti‑Drift / Anti‑Hallucination Loop

3.1 Execution Pattern

ADCCL defines the canonical loop for non‑trivial tasks:

1. Anchor: Build a GoalContract that restates the user’s task with explicit constraints, success criteria, assumptions, and unknowns.
2. Skeleton: Derive a PlanSkeleton and ClaimBudget, surfacing structure and evidence requirements before narration.
3. Ground: Retrieve memory and external documents and invoke deterministic tools to support required claims, producing EvidenceBundles and ComputeInvocationRecords.
4. Flesh: Generate a constrained draft that respects the Skeleton and ClaimBudget, preserving explicit support labels (supported / computed / inferred / hypothetical / unknown).
5. Verify: Independently evaluate the draft for grounding coverage, hallucination, structural adherence, and drift; compute a verification gate score.
6. Repair / Refuse: Attempt targeted repair under bounded retries; if verification cannot be satisfied, emit a defer/unknown result rather than fabricating.

3.2 Drift Penalty and Verification Score

During generation of a draft (y_{1:T}), ADCCL defines a drift cost:

[ J = \sum_{t=1}^{T} \big( w_s , d_s(y_t) + w_c , d_c(y_{1:t}) + w_g , g_t \big), ]

where (d_s(y_t)) measures structural violations at token (t), (d_c(y_{1:t})) measures cumulative consistency violations, and (g_t) indicates violations of guarded claims. We define this procedural drift penalty (J) over the full output sequence as a target objective for the control architecture, not as a loss the generator directly optimizes at inference time. In practice, (J) is used in two ways: (1) as the internal scoring objective for the Verifier when assessing a completed draft, and (2) as a training signal in settings where the Generator or Verifier models are trainable. At inference time, the Generator is constrained structurally by the Goal Contract and Claim Budget; it does not have access to (J) token-by-token. Minimizing (J) via rejection or repair at the verification gate stabilizes structure and constraints.

For a completed draft, a verification score is computed as:

[ V = w_r , R + w_p , P - w_u , U, ]

where (R) is a grounding/recall metric, (P) is plan or structure adherence, and (U) is unsupported‑claim burden. Publication is allowed only if (V \ge V_{\min}); otherwise the draft is routed to repair or explicit uncertainty.

3.3 Continuity Confidence

Continuity confidence across sessions is modeled as:

[ C_t = \alpha , \kappa(S_{t-1}, S_t) + (1 - \alpha) , C_{t-1}, ]

where (\kappa) measures similarity between previous and current self‑state summaries, and (C_t) determines whether to restore, fork, or quarantine prior state before serving a new session.

4 Components and Data Flow

ESS/ADCCL decomposes into a set of services, each with clear responsibilities and failure modes:

• Input Perception Layer: normalizes prompts, events, telemetry, and tool results into InputEnvelopes with timestamps and provenance.
• Goal Contract Builder: extracts task objectives, constraints, success criteria, assumptions, and unknowns, producing GoalContracts.
• Planning & Structuring Engine: produces PlanSkeletons, ClaimBudgets, and entity/dependency maps.
• Retrieval & Memory Layer: retrieves episodic and semantic memory and external documents, preserving provenance and freshness.
• Deterministic Compute Adapter: routes structured compute tasks to deterministic backends such as Wolfram Engine, returning ComputeInvocationRecords.
• Generator: renders structured state into draft text or actions without exceeding evidence bounds.
• Verifier / Critic: independently assesses drafts against GoalContracts, ClaimBudgets, and evidence; produces VerificationReports.
• Repair / Re‑plan Controller: targets recovery after verification failure without reckless scope expansion.

In a reference implementation, we recommend instantiating the Verifier as either: (a) a smaller, separately fine-tuned model trained explicitly on supported-vs.-unsupported claim classification using external ground-truth tasks, or (b) a hybrid engine in which structural checks (Goal Contract compliance, Budget coverage) are implemented as deterministic code and only semantic plausibility is delegated to an LLM. In both cases, the Verifier's prompt and temperature must differ from the Generator's, and the two processes must not share in-context state. Sharing the same prompt stream or using identical decoding parameters risks correlated failure modes and violates the ADCCL isolation requirement.

• Reflection & Experience Archive: logs outcomes, feedback, verifier reports, and summaries, and generates state deltas and SelfTags.
• Valence Learning Module: converts interaction outcomes into ValenceSignals and updates ValenceMaps.
• Self‑Tag and Pointer Chain Store: maintains SelfTags and pointer links as a formal continuity substrate.
• Inter‑Agent Comparison Layer (optional): compares normalized local meaning and valence structures across agents.

Data flows from input through anchoring, structuring, grounding, generation, verification, repair/refusal, reflection, and optional inter‑agent comparison, producing durable state suitable for long‑horizon reasoning.

5 Evaluation Plan

The evaluation plan compares ESS/ADCCL‑governed agents against baseline LLM workflows on hallucination, drift, structural adherence, and continuity.

• Drift/Hallucination Control: prompts designed to induce hallucination or scope drift; compare baseline LLM vs. ADCCL with structure‑before‑narration and verification gates. Metrics: hallucination rate, structural adherence, citation coverage, latency overhead.
• Continuity/Identity Stability: multi‑session tasks with long‑term goals; pointer‑chain on/off, SelfTag on/off. Metrics: goal drift, identity consistency score, refusal/rollback count, session rebind time.
• Lattice Stability (with MYELIN): exploratory tests of retrieval and topology health under repeated updates; metrics: Jaccard over k‑NN sets, edge‑weight drift percentiles, retrieval accuracy under drift.

These experiments should be run both in isolation (ESS/ADCCL alone) and within the full OmegA stack to isolate contributions from memory, OS, and shell layers.

6 Relationship to the OmegA Architecture Stack

Emergent Sentience Scaffolding (ESS) and the Anti‑Drift / Anti‑Hallucination Cognitive Control Loop (ADCCL) are designed as the cognitive‑control and continuity layer within a broader OmegA‑class architecture rather than as a stand‑alone agent pattern. In the canonical stack, MYELIN provides a path‑dependent graph memory substrate, AEON⭑OmegA provides identity canon and task‑state control, and the AEGIS shell provides a portable governance and identity envelope across heterogeneous models.

ESS/ADCCL assumes access to durable storage, retrieval pipelines, deterministic compute, and an operating system that can represent identity, continuity, and task state explicitly. In OmegA, those prerequisites are met as follows. MYELIN supplies a graph‑structured memory whose nodes and edges can be updated from retrieval success and interaction outcomes; ESS’s ExperienceRecords, SelfTags, and valence‑conditioned updates are stored as changes to that substrate rather than as opaque logs. AEON⭑OmegA provides Phylactery, ContinuityChain, Task State Objects (TSOs), EventStore, and Binder; ESS’s Goal Contracts, Plan Skeletons, ClaimBudgets, verification metrics, SelfTags, and ValenceMaps are realized concretely as TSO fields, continuity tags, events, and experience packets inside that OS.

The AEGIS shell wraps this entire stack. It compiles Run Envelopes that include identity kernels derived from Phylactery, Goal Contracts from ESS, governance policies, MYELIN‑backed memory snapshots, tool manifests, and audit settings before invoking any substrate model. It then executes the ADCCL loop—anchoring, structuring, grounding, generating, verifying, repairing or refusing—with enforcement power: drafts that fail verification or governance thresholds are either constrained or blocked, not silently emitted.

In this configuration, ESS/ADCCL is the discipline governing how OmegA thinks and speaks. It is neither a complete agent nor a mere router; it is the layer that keeps long‑horizon cognition anchored to explicit structure, grounded evidence, and persistent continuity over a MYELIN substrate, under an AEON OS, inside an AEGIS shell.

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