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Episodic-to-Semantic Consolidation Without Identity... | AI Research

Key Takeaways

  • Long-running intelligent agents, such as robots operating in hospitals or warehouses, face a difficult trade-off: they need to learn from their experiences t...
  • 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.
  • Traditionally, updating an agent’s knowledge—through fine-tuning, prompt rewriting, or policy distillation—mutates the very files that define its identity.
Paper AbstractExpand

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.

Long-running intelligent agents, such as robots operating in hospitals or warehouses, face a difficult trade-off: they need to learn from their experiences to become more effective, but they must also maintain a stable, cryptographically certified identity to satisfy safety and compliance audits. Traditionally, updating an agent’s knowledge—through fine-tuning, prompt rewriting, or policy distillation—mutates the very files that define its identity. This paper introduces "identity-stable consolidation," a method that allows agents to accumulate knowledge without changing their certified identity, ensuring they remain compliant while becoming smarter over time.

Separating Identity from Knowledge

The core of this approach is a structural separation between an agent’s identity and its operational memory. The researchers define the agent’s identity through a "manifest"—a set of data that is hashed into a fixed, cryptographically secure value. Any change to this manifest results in a new identity, which would trigger a costly and complex re-certification process. Instead of storing learned lessons within the manifest or the agent's core policy, the authors create a separate "semantic knowledge layer." This layer acts as a queryable database of facts derived from the agent’s past experiences, keeping it entirely outside the scope of the identity hash.

How Consolidation Works

The framework uses a deterministic function to process the agent’s episodic event log—an append-only record of every interaction—into structured semantic facts. For example, if a robot learns through repeated trials that a specific force is required to grasp a glass cup, the system aggregates these individual attempts into a single, reliable fact. This fact includes the recommended force, a confidence score, and a "provenance pointer" that allows auditors to trace the fact back to the specific events that created it. Because this process is deterministic and the semantic layer is not part of the identity hash, the agent’s cryptographic certificate remains byte-equal across every consolidation pass.

Proven Results

To validate this construction, the researchers tested it against a 1,000-decision synthetic benchmark. The results showed that the identity-stable consolidation method significantly improved performance, achieving a mean 79.82% reduction in unproductive planner attempts compared to a calibrated baseline. Crucially, the researchers verified that the agent’s identity hash remained identical before and after these consolidation passes, proving that the agent could learn and improve its decision-making without ever "drifting" from its original, certified identity.

Future Considerations

While the current version of this consolidation algorithm relies on deterministic, rule-based aggregation to ensure auditability, the authors note that more advanced methods, such as LLM-assisted consolidation, are a potential future direction. However, they caution that introducing non-deterministic elements like LLMs into the consolidation process could complicate the audit trail. For now, the focus remains on a stable, transparent, and auditable discipline that allows embodied agents to evolve in regulated environments without violating their operational commitments.

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