AI Research
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 b...
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ArXiv CS.AI
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ArXiv CS.AI
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LLM agents struggle with long contexts due to memory overload; SF-AMS addresses this by dynamically prioritizing useful information, enabling more robust multi-step reasoning and improved performance across various tasks and model architectures.
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