wuyang
63 lines
1.6 KiB
Markdown
63 lines
1.6 KiB
Markdown
# Paper: EvoMemBench
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---
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type: paper
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title: "EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective"
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authors: Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li
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year: 2026
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venue:
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url: https://arxiv.org/html/2605.18421
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code_url:
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source: arxiv
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collected_at: 2026-07-08
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status: skimmed
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relevance: high
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topics:
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- memory
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- agent-evaluation
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methods:
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- memory-benchmark
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- self-evolving-agent
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benchmarks:
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- EvoMemBench
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models:
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datasets:
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related_concepts:
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- memory
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- procedural-memory
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related_jobs:
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- 2026-07-08-baidu-aidu-agent-algorithm-engineer-beijing
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- 2026-07-08-bytedance-seed-llm-agent-research-engineer
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related_experiments:
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related_projects:
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-
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---
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## One-line Takeaway
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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。
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## Problem
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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。
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## Core Idea
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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。
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## Evidence
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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。
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## Useful For Us
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- 设计 memory 实验时要加入长上下文 baseline。
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- 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。
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## Follow-up Experiments
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- 用 Agent 项目任务集做 memory-vs-long-context 对照实验。
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