SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batche...
| Authors | Congjie Zheng, Chuanyi Xue, Bin Liang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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