SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning. Strategic Forgetting for Agent Memory Systems (SF-AMS) is proposed as a framework for maintaining compact high-utility memory by modeling the long-term importance of memory units. SF-AMS replaces static retrieval and heuristic decay with a utility-driven survival mechanism that updates memory importance from usage redundancy and temporal signals, inducing a hierarchical memory structure that prioritizes stable entity-consistent information while filtering noise. On top of this, Composite Importance Scoring integrates semantic and entity l...
| Authors | Ning Yang, Siqi Li, Miaoxin Shen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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