提出动态环境评估与记忆演化机制，提升大模型代理的持续适应能力。
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
- 设计基于分块更新的记忆架构，追踪环境变化的历史
- 在动态任务中平均准确率仅39.6%，引入新方法后提升至41.1%
- 适合关注长期适应性与环境演化的智能体研究者
大型语言模型（LLM）代理在众多基准测试中表现优异，但现有评估多假设环境静态。真实部署场景本质动态，要求代理持续对齐知识、技能与行为以应对环境变化和任务更新。为此，我们提出EvoArena基准套件，将环境变化建模为终端、软件和社交领域中的渐进式更新序列。进一步提出EvoMem，一种基于分块的记忆范式，以结构化更新历史记录记忆演化，使代理能够通过记忆变化推理环境演化过程。实验表明，当前代理在EvoArena上平均准确率为39.6%。EvoMem持续提升性能，在EvoArena上平均提升1.5%，同时在标准基准GAIA和LoCoMo上分别提升6.1%和4.8%。在需连续完成相关演化子任务的链级任务中，其准确率提升达3.7%。机制分析显示，EvoMem增强了记忆中的证据捕获能力，更好保留了持续演化的环境状态。结果凸显在评估与记忆中建模演化的必要性，以实现可靠代理部署。
原文摘要 · Abstract (English)
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.
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