AGENTIC-SYSTEMSJuly 28, 20261 min read

SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent

Researchers introduced SF-AMS, a strategic forgetting framework that maintains compact, high-utility memory for LLM agents. By replacing static retrieval with dynamic importance modeling, SF-AMS significantly improves multi-step and long-context reasoning performance.

Computer Science > Artificial Intelligence

Title:SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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