SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

Researchers introduce SEA-Eval, a new benchmark for evaluating self-evolving AI agents that go beyond single-task execution by measuring how agents improve across sequential tasks and accumulate experience over time. The benchmark reveals significant inefficiencies in current state-of-the-art frameworks, exposing up to 31.2x differences in token consumption despite identical success rates, highlighting a critical bottleneck in agent development.

The emergence of self-evolving agents represents a fundamental shift in how AI systems are designed and evaluated. Current LLM-based agents operate episodically, solving individual tasks without carrying learned insights forward or optimizing their internal processes over time. SEA-Eval addresses this limitation by introducing a formal framework that tracks agent performance across sequential task streams, measuring both immediate execution reliability and long-term evolutionary gains. This approach reveals a hidden problem in the field: existing benchmarks mask critical inefficiencies by focusing solely on success rates. Two agents achieving identical task completion rates may consume vastly different computational resources and follow divergent learning trajectories, insights that traditional episodic assessment completely obscures. The discovery of a 31.2x token consumption variance among equivalent performers demonstrates that current evaluation methodologies fail to capture crucial aspects of agent quality. For the AI development community, SEA-Eval establishes a more rigorous scientific standard for benchmarking, pushing the field toward agents that genuinely accumulate experience and optimize strategies rather than merely executing isolated tasks. This distinction matters because truly self-evolving agents could dramatically reduce computational overhead and improve practical deployment efficiency. The benchmark's formal grounding in digital embodiment provides a foundation for future research into persistent, adaptive AI systems. Looking ahead, widespread adoption of SEA-Eval could reshape how researchers prioritize agent development, shifting focus from raw performance metrics toward sustainable, efficient learning across task boundaries.

• →SEA-Eval is the first benchmark designed to measure self-evolving agent characteristics across both intra-task reliability and long-term evolutionary performance.
• →Current state-of-the-art frameworks exhibit up to 31.2x differences in token consumption despite identical success rates, revealing hidden inefficiencies.
• →Existing episodic benchmarks fail to capture how agents accumulate experience or optimize strategies across task boundaries.
• →The benchmark organizes tasks into sequential streams to quantify evolutionary gain and structural stability over time.
• →SEA-Eval establishes a formal scientific foundation for advancing AI agents from task executors toward genuinely self-evolving digital entities.