# Paper: EvoMemBench

---
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:
  -
---

## 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。
