Adaptive Memory Admission Control for LLM Agents
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...
| Authors | Guilin Zhang, Wei Jiang, Xiejiashan Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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