arXiv cs.AI· Yutong Song, Jiang Wu, Yuxuan Ren, Tony Qin, Baoyuan Wang·· 14 小时前AI 评分40
Hippo：无需训练、基于海马体回放的 LLM 智能体自我进化框架
Hippo: Training-Free Agent Self-Evolution via Hippocampal Replay
AI 导读
Hippo 是一个受海马体选择性编码与回放启发的记忆框架，让 LLM 智能体在不更新模型参数的情况下通过外部记忆实现自我进化。它通过对比同一任务的多次尝试来识别可复用决策，并与已有记忆合并重叠、以显式适用条件解决冲突，从而避免把每条经验单独存储。
正文 · 原文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.24453 [cs.AI] |
| (or arXiv:2610.24453v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.24453 arXiv-issued DOI via DataCite |
Submission history
From: Yutong Song [view email]
[v1]
Sat, 17 Oct 2026 04:43:26 UTC (873 KB)
[v2]
Thu, 29 Oct 2026 17:38:48 UTC (873 KB)
来源：arXiv cs.AI · arxiv.org