arXiv cs.CL· Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li·· 11 小时前AI 评分35
EvoMemBench：从自演化视角评测 LLM 智能体记忆能力
EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
AI 导读
研究者提出 EvoMemBench，一个按记忆范围（单回合内 vs. 跨回合）与记忆内容（知识导向 vs. 执行导向）两轴组织的 LLM 智能体记忆统一基准。该基准在标准化协议下对比了 15 种代表性记忆方法与强长上下文基线，结果显示长上下文基线仍极具竞争力，记忆在当前上下文不足或任务困难时帮助最大，且没有单一记忆形式在所有场景下稳定有效。
正文 · 原文
Abstract:Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to store, update, and retrieve information over time. This ability remains under-evaluated, largely because existing benchmarks do not provide a systematic way to assess memory mechanisms. In this paper, we study agent memory from a self-evolving perspective and introduce EvoMemBench, a unified benchmark organized along two axes: memory scope (in-episode vs. cross-episode) and memory content (knowledge-oriented vs. execution-oriented). We compare 15 representative memory methods with strong long-context baselines under a standardized protocol. Results show that current memory systems are still far from a general solution: long-context baselines remain highly competitive, memory helps most when the current context is insufficient or tasks are difficult, and no single memory form works consistently across all settings. Retrieval-based methods remain strong for knowledge-intensive settings, whereas procedural and long-term memory methods are more effective for execution-oriented tasks when their stored experience matches the task structure. We hope EvoMemBench facilitates future research on more effective memory systems for LLM-based agents. Our dataset and code are available at this https URL.
| Comments: | Accepted by NeurIPS 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.18421 [cs.CL] |
| (or arXiv:2605.18421v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18421 arXiv-issued DOI via DataCite |
Submission history
From: Yuyao Wang [view email]
[v1]
Mon, 18 May 2026 13:54:38 UTC (571 KB)
[v2]
Mon, 15 Jun 2026 07:27:08 UTC (566 KB)
[v3]
Fri, 30 Oct 2026 08:21:19 UTC (575 KB)
来源：arXiv cs.CL · arxiv.org