SEAGym: An Evaluation Environment for Self-Evolving LLM Agents

SEAGym is a newly introduced evaluation environment designed for assessing self-evolving LLM-based agents by focusing on their agent harness, which includes components like prompts, memory, and tool interactions. It provides a framework for measuring updates across various datasets and conditions, transforming existing benchmarks into dynamic sources for self-evolution tasks. The evaluation reveals that frequent updates do not necessarily lead to improved performance, highlighting the importance of diverse sources and model backends in maintaining harness reliability, which is crucial for practitioners developing adaptive AI systems.