arXiv:2606.17546v1 Announce Type: new Abstract: 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,
The rapid advancement and deployment of LLM-based agents necessitate robust evaluation frameworks to understand and direct their development effectively.
This new evaluation environment allows for more granular and accurate measurement of LLM agent improvements, which is critical for their reliable and beneficial integration into complex systems.
The ability to systematically evaluate changes in LLM agent 'harnesses' means that development can be more iterative and less prone to 'overfitting' without understanding deeper impacts.
- · AI researchers
- · LLM agent developers
- · Organizations deploying agents
- · AI evaluation platforms
- · Developers relying on ad-hoc evaluations
- · Inefficient AI agent development cycles
SEAGym provides a standardized toolkit for assessing self-evolving LLM agents, moving beyond isolated task scores.
Improved evaluation leads to more robust, cost-effective, and safe AI agents being deployed in real-world applications.
The acceleration of reliable agent development could speed up the adoption and integration of autonomous AI systems across various industries, further transforming white-collar workflows.
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Read at arXiv cs.AI