让大模型通过执行反馈不断优化数学推理过程，提升准确性。
Enhancing Mathematical Problem Solving in LLMs through Execution-Driven Reasoning Augmentation
- 迭代式构建程序链，结合执行结果修正错误。
- 在多个基准上超越现有方法，提升大模型数学解题能力。
- 适合需要可靠符号推理的教育与科研场景。
数学问题求解是评估人工智能推理能力的核心指标，也是教育、科学和工程等领域中可靠符号推理的关键应用。尽管基于多智能体的大语言模型在数学推理方面取得进展，但仍缺乏可回溯、可修正的推理表示。现有方法或采用僵化顺序流程无法修正早期错误，或依赖启发式自评估，难以发现并修复错误。此外，编程上下文会干扰语言模型，降低准确率。为此，我们提出迭代优化程序构建（IIPC）方法，通过迭代精炼程序化推理链，结合执行反馈与基座大模型的思维链能力，保持高层上下文聚焦。IIPC在多个基座大模型上的多数推理基准中表现优于现有方法。所有代码与实现均已开源。
原文摘要 · Abstract (English)
Mathematical problem solving is a fundamental benchmark for assessing the reasoning capabilities of artificial intelligence and a gateway to applications in education, science, and engineering where reliable symbolic reasoning is essential. Although recent advances in multi-agent LLM-based systems have enhanced their mathematical reasoning capabilities, they still lack a reliably revisable representation of the reasoning process. Existing agents either operate in rigid sequential pipelines that cannot correct earlier steps or rely on heuristic self-evaluation that can fail to identify and fix errors. In addition, programmatic context can distract language models and degrade accuracy. To address these gaps, we introduce Iteratively Improved Program Construction (IIPC), a reasoning method that iteratively refines programmatic reasoning chains and combines execution feedback with the native Chain-of-thought abilities of the base LLM to maintain high-level contextual focus. IIPC surpasses competing approaches in the majority of reasoning benchmarks on multiple base LLMs. All code and implementations are released as open source.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。