agent/papers/items/2026-wang-evomembench.md

Paper: EvoMemBench

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type: paper title: "EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective" authors: Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li year: 2026 venue: url: https://arxiv.org/html/2605.18421 code_url: source: arxiv collected_at: 2026-07-08 status: skimmed relevance: high topics:

• memory
• agent-evaluation methods:
• memory-benchmark
• self-evolving-agent benchmarks:
• EvoMemBench models:

datasets:

related_concepts:

• memory
• procedural-memory related_jobs:
• 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing
• 2026-07-08-bytedance-seed-llm-agent-research-engineer related_experiments:

related_projects:

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One-line Takeaway

Memory 的价值取决于任务类型和上下文约束；长上下文 baseline 仍然很强，不能假设外部记忆天然更好。

Problem

现有 Agent benchmark 主要评估 reasoning、planning、execution，对 memory 的更新、修正和复用能力覆盖不足。

Core Idea

EvoMemBench 按 memory scope 和 memory content 两条轴设计 benchmark，比较 15 种 memory 方法和长上下文 baseline。

Evidence

结果显示 memory 在当前上下文不足或任务较难时更有价值；知识密集任务更适合 retrieval memory，执行导向任务更依赖 procedural/long-term memory。

Useful For Us

• 设计 memory 实验时要加入长上下文 baseline。
• 不同岗位里提到 memory 时，要进一步区分事实记忆、过程记忆和跨任务经验。

Follow-up Experiments

• 用 Agent 项目任务集做 memory-vs-long-context 对照实验。