# EvoArena：面向动态环境的LLM智能体记忆演化基准与EvoMem记忆范式

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-11 08:00
- AIHOT 分数：67
- AIHOT 链接：https://aihot.virxact.com/items/cmqaef40q0kqpslldis833la8
- 原文链接：https://arxiv.org/abs/2606.13681

## AI 摘要

EvoArena是一个基准套件，将环境变化建模为终端、软件和社交领域的渐进更新序列，用于评估LLM智能体在动态环境中的表现。实验显示，当前智能体在EvoArena上的平均准确率仅为39.6%。EvoMem是一种基于补丁的记忆范式，通过结构化更新历史记录记忆演化，使智能体根据记忆变化推理环境演变。EvoMem在EvoArena上带来平均1.5%的性能提升，在GAIA和LoCoMo上分别提升6.1%和4.8%，并将EvoArena链级准确率提升3.7%。机制分析表明，EvoMem改善了记忆中的证据捕获，更完整地保留演化环境状态。

## 正文

Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deployment is inherently dynamic, requiring agents to continually align their knowledge, skills, and behavior with changing environments and updated task conditions. To address this gap, we introduce EvoArena, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and social domains. We further propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories, enabling agents to reason about environmental evolution through changes in their memory. Experiments show that current agents struggle on EvoArena, achieving an average accuracy of 39.6% across evolving terminal, software, and social-preference domains. EvoMem consistently improves performance, yielding an average gain of 1.5% on EvoArena and also improving standard benchmarks such as GAIA and LoCoMo by 6.1% and 4.8%. Beyond individual tasks, EvoMem further improves chain-level accuracy by 3.7% on EvoArena, where success requires completing a consecutive sequence of related evolutionary subtasks. Mechanistic analysis shows that EvoMem improves evidence capture in the memory, indicating better preservation of complete evolving environment states. Our results highlight the importance of modeling evolution in both evaluation and memory for reliable agent deployment.