[2603.04549] Adaptive Memory Admission Control for LLM Agents

Computer Science > Artificial Intelligence arXiv:2603.04549 (cs) [Submitted on 4 Mar 2026] Title:Adaptive Memory Admission Control for LLM Agents Authors:Guilin Zhang, Wei Jiang, Xiejiashan Wang, Aisha Behr, Kai Zhao, Jeffrey Friedman, Xu Chu, Amine Anoun View a PDF of the paper titled Adaptive Memory Admission Control for LLM Agents, by Guilin Zhang and 7 other authors View PDF HTML (experimental) Abstract:LLM-based agents increasingly rely on long-term memory to support multi-session reasoning and interaction, yet current systems provide little control over what information is retained. In practice, agents either accumulate large volumes of conversational content, including hallucinated or obsolete facts, or depend on opaque, fully LLM-driven memory policies that are costly and difficult to audit. As a result, memory admission remains a poorly specified and weakly controlled component in agent architectures. To address this gap, we propose Adaptive Memory Admission Control (A-MAC), a framework that treats memory admission as a structured decision problem. A-MAC decomposes memory value into five complementary and interpretable factors: future utility, factual confidence, semantic novelty, temporal recency, and content type prior. The framework combines lightweight rule-based feature extraction with a single LLM-assisted utility assessment, and learns domain-adaptive admission policies through cross-validated optimization. This design enables transparent and efficient cont...