Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given the same task five times, a ReAct agent on the AppWorld benchmark using GPT-4.1 succeeds in all five runs only 53% of the time, even though...
Evelyn Duesterwald, Benjamin Elder, Lilian Ngweta et al.
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