AIBullisharXiv – CS AI · 8h ago6/10
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RaMem: Contextual Reinstatement for Long-term Agentic Memory
Researchers introduce RaMem, a framework that solves the 'context collapse' problem in long-term LLM agent memory systems by recontextualizing retrieved memory fragments with their original episodic conditions. The approach uses evidence anchoring, condition induction, validity-aware retrieval, and context-preserved synthesis to improve memory relevance verification, achieving over 10% F1 improvement across benchmarks.