AI RESEARCH
CraniMem: Cranial Inspired Gated and Bounded Memory for Agentic Systems
arXiv CS.AI
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ArXi:2603.15642v1 Announce Type: new Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many turns. Many existing agent memory systems behave like external databases with ad hoc read/write rules, which can yield unstable retention, limited consolidation, and vulnerability to distractor content. We present CraniMem, a neurocognitively motivated, gated and bounded multi-stage memory design for agentic systems.