AMIE：无需重训的智能体记忆索引自适应插件
Adaptive Memory Index Evolution for Agents
给做长期运行 Agent 的人：AMIE 这个插件能只用检索记录就优化记忆召回，15 组对比全提分，还不用重训记忆系统。
arXiv 论文提出 Memory Index Evolution 任务，并给出轻量插件 AMIE，通过从部署时的检索行为中学习，调整每条记忆的访问状态，不改动存储内容、不重训宿主记忆系统。在 5 个检索型宿主和 3 个基准上，训练后的 AMIE 在全部 15 组对比中提升下游 F1 或准确率 0.70–3.37 个百分点（相对最高 25%），10 组检索对比全部改善。实验还发现仅累积检索频率的零调用策略在部分设置中已能匹配语义策略，说明检索流量本身就是强信号。分析显示持续压制竞争记忆可暴露此前未见证据，而压制反复有用的记忆会带来相反的失败模式。
Adaptive Memory Index Evolution for Agents
Long-horizon agents continually accumulate experience, yet retaining a memory does not guarantee access when it is needed. Existing systems can adapt both memory content and access; we isolate persistent access updates as a separately controlled adaptation layer. We formulate this target as Memory Index Evolution and instantiate it with Adaptive Memory Index Evolution (AMIE), a lightweight plug-in layer that evolves per-memory access state from deployment retrievals without modifying stored content or retraining the host memory system. AMIE distils a semantic teacher's keep/suppress judgments into a retrieval-conditioned policy and accumulates its selective actions into bounded state that reweights subsequent host retrievals. Surprisingly, even a zero-call policy that simply accumulates retrieval frequency can match or outperform semantic policies in several settings, revealing retrieval traffic itself as a strong signal for access adaptation, while learned selectivity improves robustness when frequently retrieved memories remain useful. Across five retrieval-based hosts and three benchmarks, trained AMIE improves downstream F1 or accuracy in all 15 comparisons, by 0.70--3.37 percentage points (up to 25% relative), and improves retrieval in all 10 comparisons. Controlled analyses show that persistent competitor suppression can expose previously unseen evidence, while suppressing repeatedly useful memories creates the complementary failure mode. Together, these results establish persistent memory access as a practical adaptation layer, distinct from the content it serves, in retrieval-based agent memory systems.
