这篇论文搞了个AMTFV，把数学验证拆成流程和计算两步，比直接写验证程序更稳，在多个模型上最高涨了8.3个点。
AMTFV提出将数学验证建模与具体执行解耦，通过数学工具流接口支持精确计算。该方法先让验证智能体构建工作流，再用工具代理执行精确计算，最后辅助答案判定与修正。在DeepSeek、GPT和Gemini的七种模型配置上评估五个数学推理数据集，AMTFV总体优于基线。相比最强基线，单个模型配置下平均准确率最高提升8.3个百分点，中等和高等验证复杂度样本上提升更大。
AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction
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 verification workflow, encodes the mathematical objects and computational intent requiring reliable execution in an MTF request, and sends it to the mathematical toolbox agent. The latter parses the request, generates executable calls, and dispatches them to the backend for exact computation. Tool outputs then support candidate-answer adjudication, answer revision, and verification-workflow revision. We evaluate AMTFV on five challenging mathematical reasoning datasets with seven model configurations from DeepSeek, GPT, and Gemini. Experimental results show that AMTFV outperforms the representative baselines evaluated in this study overall; under an individual model configuration, it improves average accuracy over the strongest baseline by up to 8.3 percentage points, with larger gains on samples of medium and high verification complexity.