Episodic-to-Semantic Consolidation Without Identity Drift

Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity. Memory consolidation is conventionally treated as an agent-changing operation: a model is fine-tuned, a prompt rewritten, a policy distilled, or a reflection appended to the context that governs future behaviour. In regulated autonomic deployment this is a liability because the agent operates under commitments and audit contracts that bind to a specific, cryptographically certified identity. We propose to treat consolidation not as a mutation of the planner or the identity manifest, but as a deterministic function f: M^ep -> M^sem over episodic memory whose output is a separately addressable semantic knowledge layer; the identity hash does not read M^sem, so consolidation updates knowledge without changing the agent's certified identity. We give a formal account of the agent representation, prove identity invariance through a structural lemma on the manifest's hash-input set, specify a deterministic aggregation algorithm whose outputs are auditable database rows with explicit confidence and supporting-event provenance, and validate the construction with synthetic experiments demonstrating per-field correctness, byte-equal identity across consolidation passes, and a mean 79.82% reduction in unproductive planner attempts (95% BCa CI [78.02%, 81.49%] across 10 seeds) against a calibrated Bayesian-shrunk baseline. The construction is a knowledge-update discipline for autonomic agents in which lessons accumulate as queryable facts while the agent's certified identity remains byte-equal across its operational lifetime, with an embodied service agent as the running case study.

• The paper presents an identity-invariant consolidation method that decouples knowledge updates from the agent’s certified identity using a deterministic algorithm.
• It formalizes a structural identity-invariance lemma ensuring that episodic data consolidation leaves the manifest hash unchanged, thereby enhancing auditability.
• Empirical results demonstrate a 79.8% reduction in unproductive actions and confirm production-level efficiency and scalability in regulated environments.

Identity-Invariant Episodic-to-Semantic Consolidation in Adaptive Agents

Problem Setting and Motivation

The work addresses a critical tension for long-running adaptive agents deployed in regulated environments—such as healthcare, manufacturing, and logistics—where continual adaptation to operational experience must be balanced against the need for strict identity invariance in compliance with governance, auditing, and certification. Conventional knowledge consolidation mechanisms (fine-tuning, prompt engineering, policy distillation, in-context summarization) inherently alter the agent's deployed artefact, thereby mutating the cryptographically certified identity and triggering expensive recertification. This creates operational liabilities both for agent operators (identity drift) and auditors (continuous certificate management).

The paper formalizes the stability-plasticity dilemma within the unique constraints of autonomic-computing governance, extending the classical episodic/semantic memory framework with strong guarantees on information integrity and auditability.

Architectural Framework

The proposed solution separates agent state into four elements:

1. Identity Manifest ($M$): A fixed, type-safe manifest (dataclass) hashed to yield the agent's cryptographically certified identity.
2. Episodic Store ($M^{ep}$): An append-only log of timestamped operational events.
3. Semantic Store ($M^{sem}$): A derived, queryable layer of consolidated semantic facts, computed as $M^{sem} = f(M^{ep})$ via a deterministic, versioned consolidation function $f$.
4. Planner ($\pi$): An operational controller that queries (read-only) the semantic layer for grounding planner decisions, without any authority to mutate the manifest or semantic store.

The manifest hash $h = \mathrm{SHA256}(M)$ is strictly constructed from the manifest’s fields, ensuring that consolidation over $M^{ep}$ to produce $M^{sem}$ cannot, by construction, alter the identity. This identity-orthogonalization of knowledge storage foundationally diverges from prior approaches in continual/adaptive learning, which collapse knowledge and identity.

Formal Guarantees and Auditability

The primary theoretical contribution is a structural identity-invariance lemma: any consolidation function $f$ in the admissible class $M^{ep}$0 (writing only to $M^{ep}$1) leaves both the manifest $M^{ep}$2 and its hash $M^{ep}$3 syntactically and byte-for-byte unchanged. The agent's cryptographically certified identity is thus fully decoupled from its knowledge consolidation pathway. This is proved through manifest field-set inspection and enforced by type-level and schema design, not by runtime assertion—strengthening the audit and compliance story.

Every semantic fact is materialized as a database row with explicit confidence, observation counts, and provenance pointers to supporting episodic events. The pipeline is deterministic and idempotent: repeated passes over the same episodic history (with a fixed rule version) yield byte-equal semantic tables. The planner interface exposes these rows via an authenticated, read-only endpoint, with all consultations logged for end-to-end auditability.

Deterministic Consolidation Algorithm

The v1 consolidation algorithm is realized as a SQL-style aggregation pass:

• Schema: Semantic facts keyed by $M^{ep}$4, capturing skill-specific success rates, object properties, interaction patterns, and risk profiles.
• Aggregation: Episodic events are grouped by stable keys; statistics (counts, success rates, parameter means, common failure reasons) are aggregated; confidence scores are computed from observation counts and within-group variance.
• Deconfliction: Only structured episodic payloads are processed—free-form linguistic abstraction is out of v1 scope; chronology and event ordering have no effect on output due to commutative-associative aggregates.
• Audit Chain: Every consolidation pass is checkpointed in the episodic store, supporting causal traceability from semantic fact to event source.

Empirical Validation

Evaluations on synthetic benchmarks confirm all correctness, invariance, and performance claims:

• Identity Hash Invariance: The manifest hash remains byte-equal across arbitrary consolidation sequences; synthetic modifications to manifest fields mutate the hash, confirming no false invariance.
• Planner Efficiency: Grounding planner decisions with consolidated semantic facts yields a mean $M^{ep}$5 reduction in unproductive attempts (95% BCa CI $M^{ep}$6 across 10 seeds; $M^{ep}$7 runs per seed), compared to a Bayesian-shrunk calibrated baseline. Removal of the confidence signal ablates the effect.
• Robustness: Effects are insensitive to reasonable variations in confidence thresholds, Bayesian prior configurations, and problem scale, as evidenced by extensive ablation.
• Operational Overhead: Consolidation is production-cheap in resource usage, scaling sub-linearly with input size, and imposes negligible latency ($M^{ep}$8 ms for $M^{ep}$9 episodic events on commodity hardware).

Comparison to Related Work

This approach situates itself against:

• Autonomic and self-adaptive agent architectures (MAPE-K, Rainbow [kephart-2003-autonomic, garlan-2004-rainbow]), which do not address cryptographic identity invariance in memory consolidation.
• Continual and lifelong learning literature (EWC, LwF, GEM [kirkpatrick-2017-ewc, li-2018-lwf, lopez-paz-2017-gem]) which presuppose weights/policy as the agent identity, therefore inherently coupling adaptation with identity drift.
• LLM agent memory architectures (Reflexion [reflexion], Voyager [voyager], MemGPT [memgpt], MemoryBank [zhong-2024-memorybank]), in which memory consolidation is typically prompt or weight-bound, lacking cryptographic isolation.

By information-theoretic design, the proposed method guarantees zero mutual information (in the Cover-Thomas sense [cover-2006-information-theory]) between the manifest hash and semantic store content, securing agent identity against any sequence of consolidation updates.

Practical and Theoretical Implications

Pragmatically, this unconstrains adaptive agent deployment in regulated and safety-critical domains, enabling continuous calibration and optimization without recertification. The operational regime now supports agents that are operationally changed (improved behavior due to accumulated knowledge) but never identity-changed (certificate remains constant), realizing sustainable, auditable learning systems.

Theoretically, the work demarcates a new axis in agent architectures: knowledge consolidation can be made identity-orthogonal and audit-traceable, shifting the compliance and assurance problem from continuous re-certification to rule governance and version management. This aligns with modern governance doctrines where adaptation must be both explainable and auditable without undermining deployment provenance.

Future Directions

The deterministic v1 algorithm omits several expressivity features:

• LLM-Assisted Abstraction: Linguistic summarization of free-form episodic reports via LLMs is a marked future direction. The challenge lies in reconciling non-determinism and model provenance with auditability and identity invariance. Prerequisites include provenance signing, human-in-the-loop review workflows, bounded vocabularies, and stable rule baselines.
• Decay/Forgetting Mechanics: Integrating principled forgetting or temporal decay into confidence estimation while preserving byte-stable reproducibility presents new algorithmic challenges.
• Adversarial Robustness: Trusted episodic substrates are assumed; future extensions will need cryptographic provenance for episodic events to harden against poisoning attacks.
• Governed Cross-Identity Learning: Fact transfer between agent identities under explicit operator mediation is an open question for fleet-wide knowledge sharing.

Conclusion

The proposed architecture demonstrates that episodic-to-semantic consolidation, when realized as an identity-orthogonal, deterministic function, achieves substantial behavioral improvements while maintaining byte-equal agent identity across all consolidation passes. The separation between operational adaptability and identity invariance enables scalable deployment of adaptive agents subject to strong information-integrity and audit requirements. This regime—operationally changed but identity-unchanged—marks a fundamental advance in the sustainable governance of autonomous AI systems.

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Reference: "Episodic-to-Semantic Consolidation Without Identity Drift" (2607.01988)