Counterexample Guided Learning in the Large using Reasoning Agents

In three linesStudy on counterexample-guided learning to improve LLMs on regex induction tasks. Researchers propose refinement strategies (regularization, symbolic counterexample clustering) and reflection/repair loops. Results: success rates improve from 3.2% to 38.1% and 38.9% to 74.1% on hardest task groups.

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