SEA-Eval Exposes Hidden Inefficiencies in Current AI Agents
Researchers introduce SEA-Eval, the first benchmark for evaluating self-evolving agents that can learn and improve across multiple tasks over time, rather than treating each task in isolation. Current LLM-based agents perform well on individual tasks but fail to accumulate experience or adapt their toolsets, creating a gap between episodic performance and genuine long-term learning. The benchmark measures both immediate execution reliability and evolutionary gains by analyzing success rates and token consumption across sequential task streams, revealing that identical success rates can mask up to 31.2 times differences in efficiency and divergent learning patterns.
Researchers introduce SEA-Eval, the first benchmark for evaluating self-evolving agents that can learn and improve across multiple tasks over time, rather than treating each task in isolation. Current LLM-based agents perform well on individual tasks but fail to accumulate experience or adapt their toolsets, creating a gap between episodic performance and genuine long-term learning. The benchmark measures both immediate execution reliability and evolutionary gains by analyzing success rates and token consumption across sequential task streams, revealing that identical success rates can mask up to 31.2 times differences in efficiency and divergent learning patterns.
- SEA-Eval is the first benchmark designed to measure self-evolving agent capabilities across intra-task execution and long-term cross-task performance
- Current state-of-the-art agent frameworks show identical success rates but consume vastly different token amounts and follow different evolutionary trajectories
- The benchmark organizes tasks into sequential streams to capture how agents accumulate experience and optimize strategies over time, moving beyond episodic evaluation
- Empirical testing reveals a significant evolutionary bottleneck in existing frameworks, suggesting current agents are not genuinely learning across task boundaries
Most agent benchmarks measure performance on isolated tasks, missing a critical capability: whether agents can learn from experience and improve over time. SEA-Eval addresses this gap by providing a rigorous framework for evaluating genuine self-evolution, which is essential if LLM-based agents are to move beyond task executors toward systems that accumulate knowledge and adapt strategies. This work establishes a scientific foundation for measuring progress toward agents that behave more like learning systems than stateless task runners.
- Existing agent evaluation methods are insufficient for production systems, as they fail to capture efficiency degradation and learning stagnation that only appear over sequential task execution
- The 31.2x token consumption variance at identical success rates suggests current frameworks lack mechanisms to consolidate knowledge or prune redundant operations across tasks
- Future agent development must prioritize cross-task learning and tool refinement as first-class design goals, not afterthoughts, to move beyond the identified evolutionary bottleneck
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