AINeutralarXiv – CS AI · Apr 136/10
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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.