Equation10K: Teacher-Style Error Diagnosis in Handwritten Arithmetic Solutions

Vision-language models (VLMs) are increasingly considered for educational assessment, yet existing mathematical benchmarks mostly ask models to solve a problem, recognize handwritten expressions, or locate a single error. A teacher must do more: read a multi-line handwritten solution, verify each transformation, identify the earliest misconception, distinguish a new mistake from downstream error propagation, and remain reliable across diverse arithmetic structures and handwriting styles. We introduce \dataset, a procedurally generated benchmark for fine-grained diagnosis of handwritten step-by-step arithmetic. The benchmark contains 10,000 solution images spanning 10 problem families, five difficulty levels, 12 handwriting styles, and 4,272 operator-structure templates. Each solution is paired with executable step traces and labels for correctness, first-error position, and five diagnostic states: wrong value, wrong operator, incorrect order of operations, calculation error, and propagated error. We evaluate representative proprietary and open VLMs using step correctness, error-type accuracy, joint step accuracy, exact solution grading, and first-error localization. Our experiments reveal two consistent patterns: joint diagnostic accuracy decreases at higher structural difficulty levels, and performance improves when handwritten inputs are replaced by cleaner printed or oracle-text representations, while non-trivial errors remain even under oracle text. These results indicate that both visual transcription and step-level process verification remain challenging for current VLMs. \dataset provides a scalable testbed for studying multimodal process supervision and reliable AI-assisted grading. We open source the project at https://anonymous.4open.science/r/Equation10K-61BE.