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The paper proposes a method for proactively detecting implicit conflicts in user-side human-LLM dialogue, introducing a benchmark and synthesis approach to enhance lightweight LLMs' performance.
This paper identifies a critical failure mode in LLM agents where they fail to update personalized memories when new evidence conflicts with prior beliefs. It introduces the STALE benchmark and a three-dimensional probing framework, revealing that even the best models achieve only 55.2% accuracy, and proposes CUPMem as a prototype for robust memory revision.