AI记忆不是翻档案，是重建现场
我们总以为AI的记忆像硬盘，存什么就取什么。这篇论文说：不对，记忆是重建出来的。研究者给AI造了一张「线索-标签-内容」的关联网，让AI在推理时像侦探一样，根据已经找到的线索动态调整搜索路径，而不是死板地先检索再推理。在长对话测试中，这套方法准确率最高提升23%，还省了算力。它不是你明天能用上的，但它改了一个底层假设：记忆不是回放，是重构。
📄 原文摘要(英文)
Despite recent progress, LLM agents still struggle with reasoning over long interaction histories. While current memory-augmented agents rely on a static retrieve-then-reason paradigm, this rigid pipeline design prevents them from dynamically adapting memory access to intermediate evidence discovered during inference. To bridge this gap, we propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism. We represent memory as a Cue-Tag-Content graph, where associative tags serve as semantic bridges connecting fine-grained cues to memory contents. Operating on this structure, our active reconstruction mechanism integrates LLM reasoning directly into memory access, allowing the agent to iteratively explore and prune retrieval paths based on accumulated evidence. This ensures that memory retrieval is dynamically adapted to the reasoning context while avoiding combinatorial explosion caused by unconstrained expansion. Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines (up to 23%), while substantially reducing token and runtime cost, highlighting the effectiveness of active and associative reconstruction for long-horizon memory reasoning.