REMIND: Learning Evidence-Conditioned Memory Invocation over Long-Term Conversations

Long-term conversation memory is fundamental for Large Language Models (LLMs) to maintain coherent personalization and deliver contextually grounded responses across extended interactions. Recent work addresses this through retrieval-augmented generation, retrieving only question-relevant information from the memory bank to avoid hallucinations caused by irrelevant contextual noise. However, the most critical memories for answering a question are often not the most semantically similar ones. Memory relevance also arises from causal relations and temporal recency. A semantically distant memory can capture the root cause of a situation, whereas a highly similar one may reflect an obsolete state. Based on this novel insight, we present REMIND, a reinforcement-learning framework for selective, on-demand access to personal memory. Specifically, REMIND interleaves two processes on demand. Evocation constructs adaptive cues from the question and accumulated evidence, and Invocation accesses typed memory banks and inspects the returned evidence to decide whether to recall further or answer. Across 2,386 questions from LoCoMo, LongMemEval, and MemoryAgentBench, REMIND with a Qwen3-8B backbone achieves relative improvements of 12.5% over Base RAG and 4.8% over Search-R1 with same backbone. This gain highlights the value of adaptive, evidence-conditioned recall over fixed retrieval.