SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
SF-AMS proposes a strategic forgetting framework for LLM agents that models memory importance as a dynamic utility signal, replacing static retrieval with a survival mechanism and integrating composite importance scoring. The method achieves significant gains on LoCoMo and LongMemEval-s, with up to +9.65 F1 in multi-hop reasoning under Qwen2.5-7B, demonstrating strong generalization across backbones.
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[Submitted on 29 May 2026]
Title:SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
View a PDF of the paper titled SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent, by Ning Yang and 6 other authors
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Abstract: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 level signals to improve retrieval robustness. Experiments on LoCoMo and LongMemEval-s show consistent gains over strong state of the art baselines including LightMem MemO and A-Mem. The largest improvement appears in multi-hop reasoning under Qwen2.5-7B where SF-AMS achieves plus 9.65 F1 over the strongest baseline followed by temporal reasoning under GPT-4o-mini plus 6.91 F1 and open-domain tasks plus 6.53 F1 demonstrating strong cross backbone generalization. These results show that modeling memory importance as a dynamic utility signal is critical for reliable long-context reasoning.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22562 [cs.AI]
(or arXiv:2607.22562v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22562
arXiv-issued DOI via DataCite
Submission history
From: Siqi Li [view email] [v1] Fri, 29 May 2026 08:03:20 UTC (914 KB)
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