SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment
Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, failing to accumulate experience across task boundaries. This paper formalizes the Self-Evolving Agent (SEA) from the perspective of digital embodiment and continuous cross-task evolution, introduces the Evolutionary Flywheel as its minimal sufficient architecture, and presents SEA-Eval -- the first benchmark designed specifically for evaluating SEAs. Grounded in Flywheel theory, SEA-Eval establishes SR and T as primary metrics and, through sequential task stream design, is designed to quantify evolutionary gain, evolutionary stability, and imp...
| Authors | Sihang Jiang, Lipeng Ma, Zhonghua Hong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 24 May 2026 |
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