MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs
This study addresses the challenge of automated fine-grained error localization in lengthy mathematical reasoning processes. Methodologically, it introduces a two-stage LLM-based diagnostic paradigm: first, prompt-guided chain-of-thought reasoning identifies logical errors at each step; second, cross-modal error attribution integrates formula visual recognition (CV) with multi-step consistency verification. The system supports reference-free open-ended evaluation and generates interpretable feedback. Key contributions include the first prompt-driven stepwise diagnostic framework, a reference-answer-free open scoring mechanism, and a vision–language collaborative error attribution model. Evaluated on computational and word problems, the system achieves 92.7% accuracy in erroneous-step identification—outperforming baselines by 31.4 percentage points. It has been deployed in intelligent tutoring platforms across three secondary schools.