arXiv AI By Xue Qin, Simin Luan, Cong Yang, Zhijun Li

Episodic-to-Semantic Consolidation Without Identity Drift

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arXiv:2607. 01988v1 Announce Type: new Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Aug 18

MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories

arXiv:2608. 16357v1 Announce Type: cross Abstract: Autonomous agents share a transport and can call each other's tools, but they cannot share what they know: no protocol lets two agents' memories reconcile a fact phrased two ways, link related facts held apart, or reconcile contradictory knowledge without silently discarding either claim.

By Lauri Lov\'en, Jaakko Sauvola, Jukka Riekki, Sasu Tarkoma
arXiv AI
Sep 18

When AI Agents Commit: Cognitive Serializability Across Data, Evidence, Policy, and Authority

The paper introduces the concept of Cognitive Serializability for autonomous AI agents, ensuring that mutations derived from dynamic inputs—such as database reads, evidence, policy, beliefs, and delegated authority—are committed in a serial, logically consistent order. It presents a framework called Trusted Cognitive Transaction (TCT) that combines immutable executable definitions, sealed envelopes, guard-first commits, and receipt-driven reconciliation to enforce serializability and prevent anomalies. Experimental results show that the prototype implementation incurs minimal overhead while eliminating injected anomalies.

By Jun He, Deying Yu