From Interference to Evidence: Dual-Stage Memory Reactivation for Long-Term Agents

Memory systems enable LLM agents to deepen their understanding of users, continue tasks across sessions, and draw on accumulated experience in new situations. As interaction histories grow and evolve, maintaining this continuity requires both preserving past information and adapting its use to the current context. However, semantically relevant but outdated or event-mismatched memories may be misused as supporting evidence, a failure we term Positive Misreactivation. Inspired by human memory reactivation, we propose DREAM (Dual-stage REActivation Event Memory), which organizes history into a silent event substrate and controls its influence through dual-stage reactivation. For each query, a repulsive encoding gate converts potentially misleading memories into contrastive constraints, and an attractive decision gate selects valid support under these constraints. The resulting compact evidence state guides generation, with source details retrieved on demand. Across memory benchmarks, DREAM performs best among evaluated baselines; with Qwen3-30B, it improves by 6.53 F1 points on LoCoMo and 5.33 accuracy points on LongMemEval. With GPT-4o-mini, it uses 89.4% fewer answer-facing memory tokens than full-context prompting. Our work challenges the implicit equivalence between memory relevance and evidential support, establishing controlled reactivation as a more reliable paradigm for long-term conversational memory.