Hippo：基于海马回放的免训练智能体自我进化
Hippo: Training-Free Agent Self-Evolution via Hippocampal Replay
Yutong Song, Jiang Wu, Yuxuan Ren, Tony Qin, Baoyuan Wang
AI 速览：受海马选择性编码与回放启发，Hippo 通过比较同一任务的多次尝试来提炼可复用经验，并在不更新模型参数的情况下实现 LLM 智能体的外部记忆学习。
Motivated by recursive self-improvement (RSI), we study how LLM agents can learn through external memory without updating model parameters. Extracting useful experience from a single attempt is difficult: flawed reasoning can produce success, while poor execution can obscure a sound strategy. Preserving its value over time is also difficult: our accumulation baseline without consolidation initially improves but eventually performs below a no-memory baseline. Our key idea is to compare attempts at the same task to identify reusable decisions and refine when they apply. We introduce Hippo, a memory framework inspired by selective hippocampal encoding and replay that learns from these comparisons when shortcomings are diagnosed. It compares the resulting experience with existing memory to merge overlaps and resolve conflicts through explicit applicability conditions. This allows prior guidance to be revised without storing every lesson as a separate entry. Experiments on SWE-bench Verified, WebArena, and Mind2Web show performance gains in all three settings and substantially fewer retained experiences than the accumulation baseline. The gains extend across the evaluated base models of varying capabilities, indicating that memory-based adaptation remains useful even for highly capable models.