Counterexamples as Feedback for Agent Self-Correction

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code generationself-correctionfeedback mechanisms

2609.02892

Builder Relevance

80%

1h ago

Abstract

This paper presents A-CEGIS, a framework that uses counterexamples as feedback for evaluating multi-turn refinement in natural-language-to-regex synthesis.

Reality Card

Core Claim

A-CEGIS enables agents to solve 90% of tasks within a four-turn budget using diagnostic counterexample feedback, significantly outperforming other methods.

Method / Result

90% task success rate within a four-turn ablation budget.

Limitations

The paper does not specify the authors or provide detailed reproducibility metrics.

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