dorsal/arxiv
View SchemaSketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models
| Authors | Yuhang Su, Mei Wang, Yaoyao Zhong, Guozhang Li, Shixing Li, Yihan Feng, Hua Huang |
|---|---|
| Categories | |
| ArXiv ID | 2601.06944vv1 |
| URL | https://arxiv.org/abs/2601.06944 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature of human-generated sketches. This limitation is particularly pronounced in the underexplored task of visual grading, where models should not only solve a problem but also diagnose errors in hand-drawn diagrams. Such diagnostic capabilities depend on complex structural, semantic, and metacognitive reasoning. To bridge this gap, we introduce SketchJudge, a novel benchmark tailored for evaluating MLLMs as graders of hand-drawn STEM diagrams. SketchJudge encompasses 1,015 hand-drawn student responses across four domains: geometry, physics, charts, and flowcharts, featuring diverse stylistic variations and distinct error types. Evaluations on SketchJudge demonstrate that even advanced MLLMs lag significantly behind humans, validating the benchmark's effectiveness in exposing the fragility of current vision-language alignment in symbolic and noisy contexts. All data, code, and evaluation scripts are publicly available at https://github.com/yuhangsu82/SketchJudge.
{
"annotation_id": "16789d6f-dcb9-4e98-b763-c15f2312ec15",
"date_created": "2026-02-17T05:53:08.513000Z",
"date_modified": "2026-02-17T05:53:08.513000Z",
"file_hash": "cbbb07ad538d020e56a96ff8b4905c1db6a0cd2270f9a5ff32447ef0080ae436",
"private": false,
"record": {
"abstract": "While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature of human-generated sketches. This limitation is particularly pronounced in the underexplored task of visual grading, where models should not only solve a problem but also diagnose errors in hand-drawn diagrams. Such diagnostic capabilities depend on complex structural, semantic, and metacognitive reasoning. To bridge this gap, we introduce SketchJudge, a novel benchmark tailored for evaluating MLLMs as graders of hand-drawn STEM diagrams. SketchJudge encompasses 1,015 hand-drawn student responses across four domains: geometry, physics, charts, and flowcharts, featuring diverse stylistic variations and distinct error types. Evaluations on SketchJudge demonstrate that even advanced MLLMs lag significantly behind humans, validating the benchmark\u0027s effectiveness in exposing the fragility of current vision-language alignment in symbolic and noisy contexts. All data, code, and evaluation scripts are publicly available at https://github.com/yuhangsu82/SketchJudge.",
"arxiv_id": "2601.06944",
"authors": [
"Yuhang Su",
"Mei Wang",
"Yaoyao Zhong",
"Guozhang Li",
"Shixing Li",
"Yihan Feng",
"Hua Huang"
],
"categories": [
"cs.CV",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models",
"url": "https://arxiv.org/abs/2601.06944",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4b7f119d-3ba4-4693-bb2d-0fb2b8f631cb",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}