SF-AMS: Strategic Forgetting for LLM Agent Memory
A novel approach known as Strategic Forgetting for Agent Memory Systems (SF-AMS) tackles the challenges posed by long-context dependencies in LLM agents. It substitutes traditional static retrieval and heuristic decay with a utility-based survival mechanism. SF-AMS evaluates the long-term significance of memory units, adjusting their importance according to usage redundancy and temporal cues, thereby establishing a hierarchical memory framework that emphasizes stable, entity-consistent data while minimizing irrelevant information. The Composite Importance Scoring method combines semantic and entity-level signals to enhance retrieval effectiveness. Testing on LoCoMo and LongMemEval-s benchmarks demonstrates significant improvements over robust baselines like LightMem, MemO, and A-Mem, particularly in multi-hop reasoning with Qwen2.5-7B. The research can be accessed on arXiv under ID 2607.22562.
Key facts
- SF-AMS is a framework for managing long-context dependencies in LLM agents.
- It uses a utility-driven survival mechanism instead of static retrieval or heuristic decay.
- Memory importance is updated from usage redundancy and temporal signals.
- Composite Importance Scoring integrates semantic and entity-level signals.
- Experiments on LoCoMo and LongMemEval-s show gains over LightMem, MemO, and A-Mem.
- Largest improvement in multi-hop reasoning under Qwen2.5-7B.
- Paper available on arXiv: 2607.22562.
Entities
Institutions
- arXiv