EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
From Daily Paper Cast by Jingwen Liang, Gengyu Wang
June 13, 2026 · 24 min · Episode 1967
About this episode
The episode discusses EvoArena, a benchmark for evaluating LLM agents in dynamic environments and introduces EvoMem, a memory paradigm that enhances agent performance.
🤗 Upvotes: 105 | cs.CL Authors: Jundong Xu, Qingchuan Li, Jiaying Wu, Yihuai Lan, Shuyue Stella Li, Huichi Zhou, Bowen Jiang, Lei Wang, Jun Wang, Anh Tuan Luu, Caiming Xiong, Hae Won Park, Bryan Hooi, Zhiyuan Hu Title: EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments Arxiv: http://arxiv.org/abs/2606.13681v1 Abstract: 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…
People in this episode
Hosts: Jingwen Liang, Gengyu Wang
Topics covered
- memory evolution
- LLM agents
- dynamic environments
- benchmarking
- performance improvement
Keywords
- EvoArena
- LLM agents
- memory evolution
- dynamic environments
- benchmark suite
- EvoMem
- performance improvement
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