EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
要約
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 envir…