FSFM is a biologically-inspired selective forgetting framework for LLM agents that claims to boost access efficiency by 8.49%, content quality by 29.2% signal-to-noise, and eliminate security risks entirely through a taxonomy of decay, deletion, safety, and adaptive mechanisms.
AllMem: A Memory-centric Recipe for Efficient Long-context Modeling
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FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory
FSFM is a biologically-inspired selective forgetting framework for LLM agents that claims to boost access efficiency by 8.49%, content quality by 29.2% signal-to-noise, and eliminate security risks entirely through a taxonomy of decay, deletion, safety, and adaptive mechanisms.