Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
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TL;DR - This paper introduces a self-evolving agent framework that learns from unstable trajectory steps to make repeated executions more consistent. On AppWorld, it substantially improves the share of tasks that succeed across all five runs.
- Defines the “consistency gap”: ReAct with GPT-4.1 averages a 77% per-run pass rate, but succeeds in all five attempts on only 53% of tasks.
- A Consistency Analyzer identifies steps likely to produce divergent outcomes across executions.
- A Guideline Generator converts these diagnoses into targeted episodic memories for future runs on related tasks.
- Five-run success rises by 16 percentage points on same-task evaluation and 13 points on similar-task generalization.
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Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
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TL;DR - This paper introduces a self-evolving agent framework that learns from unstable trajectory steps to make repeated executions more consistent. On AppWorld, it substantially improves the share of tasks that succeed across all five runs.
- Defines the “consistency gap”: ReAct with GPT-4.1 averages a 77% per-run pass rate, but succeeds in all five attempts on only 53% of tasks.
- A Consistency Analyzer identifies steps likely to produce divergent outcomes across executions.
- A Guideline Generator converts these diagnoses into targeted episodic memories for future runs on related tasks.
- Five-run success rises by 16 percentage points on same-task evaluation and 13 points on similar-task generalization.