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Dual-Process Scaffold Reasoning for Enhancing LLM Code Debugging

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent publication of the Dual-Process Scaffold Reasoning framework for code debugging highlights a significant advancement in the application of psychological theories to artificial intelligence. By integrating cognitive pathways into the debugging process, this framework achieves an impressive 88.91% pass rate and an average inference time of 5.36 seconds on DebugBench, outperforming other reasoning approaches across various LLMs. The framework's focus on System 2 reasoning addresses a gap in existing research, as this aspect has not been fully explored. The findings suggest a strong alignment with human cognitive processes, indicating that LLMs can be further optimized for complex problem-solving tasks. This development not only enhances the debugging capabilities of LLMs but also contributes to the broader understanding of how psychological insights can improve AI performance.
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