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

SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment arXiv:2604.08988v3 Announce Type: replace Abstract: 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 Evolutionar