We propose utilizing fast and slow thinking to enhance the capabilities of large language model-based agents on complex tasks such as program repair. In particular, we design an adaptive program repair method based on issue description response, called SIADAFIX. The proposed method utilizes slow thinking bug fix agent to complete complex program repair tasks, and employs fast thinking workflow decision components to optimize and classify issue descriptions, using issue description response results to guide the orchestration of bug fix agent workflows. SIADAFIX adaptively selects three repair modes, i.e., easy, middle and hard mode, based on problem complexity. It employs fast generalization for simple problems and test-time scaling techniques for complex problems. Experimental results on the SWE-bench Lite show that the proposed method achieves 60.67% pass@1 performance using the Claude-4 Sonnet model, reaching state-of-the-art levels among all open-source methods. SIADAFIX effectively balances repair efficiency and accuracy, providing new insights for automated program repair. Our code is available at https://github.com/liauto-siada/siada-cli.

翻译：我们提出利用快慢思维来增强基于大语言模型的智能体在程序修复等复杂任务上的能力。具体而言，我们设计了一种基于问题描述响应的自适应程序修复方法，称为SIADAFIX。该方法利用慢思维的错误修复智能体完成复杂的程序修复任务，并采用快思维的工作流决策组件对问题描述进行优化和分类，利用问题描述响应的结果来指导错误修复智能体工作流的编排。SIADAFIX根据问题复杂度自适应地选择三种修复模式，即简单、中等和困难模式。对于简单问题采用快速泛化策略，对于复杂问题则运用测试时扩展技术。在SWE-bench Lite基准测试上的实验结果表明，使用Claude-4 Sonnet模型时，所提方法达到了60.67%的pass@1性能，在所有开源方法中达到了最先进水平。SIADAFIX有效平衡了修复效率与准确性，为自动化程序修复提供了新的思路。我们的代码可在https://github.com/liauto-siada/siada-cli获取。