AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction· AMTFV：基于数学工具流的LLM自校正
Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging. Existing representative methods mainly revise outputs through natural-language reflection or assist verification by directly generating verification programs; the former may not reliably support exact computation, whereas the latter prematurely couples mathematical modeling with low-level implementation. We propose AMTFV (Agentic Mathematical Tool-Flow Verification). By introducing Mathematical Tool Flow (MTF) as an interrupt--execute--resume interface, AMTFV decouples verification modeling from concrete execution and supports exact computation through a mathematical toolbox. Specifically, the verification agent first constructs a ver
提出一种基于数学工具流的验证方法，用于大型语言模型的自校正。
- 核心方法
- 引入数学工具流(MTF)作为中断-执行-恢复接口，将验证建模与具体执行解耦，通过数学工具箱支持精确计算。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 大型语言模型在解决数学问题时，验证其答案的可靠性具有挑战性。
- 关键实验
- 实验显示AMTFV在多个数学问题上显著提高了模型自校正的准确性，但具体实验数据未详细提供。
- 主要贡献
- 提出AMTFV框架，提高了大型语言模型解决数学问题时答案的可靠性，同时支持精确计算。
- 意义与局限
- AMTFV框架为大型语言模型提供了一种有效的数学问题自校正方法，但其依赖特定的数学工具箱可能限制了模型的灵活性。