README.md

metadata

license: apache-2.0
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - image-to-text
  - visual-question-answering
  - text-generation
tags:
  - scientific-diagrams
  - diagram-understanding
  - diagram-to-code
  - tikz
  - latex
  - code-generation
  - diagram-editing
  - chart-understanding
  - multimodal
  - benchmark
pretty_name: Diagram-MMU
annotations_creators:
  - expert-generated
source_datasets:
  - original
configs:
  - config_name: diagrams
    data_files:
      - split: test
        path: data/diagrams/test-*.parquet
      - split: testmini
        path: data/diagrams/testmini-*.parquet
  - config_name: d2c-p
    data_files:
      - split: test
        path: data/d2c-p/test-*.parquet
      - split: testmini
        path: data/d2c-p/testmini-*.parquet
  - config_name: d2c-e
    data_files:
      - split: test
        path: data/d2c-e/test-*.parquet
      - split: testmini
        path: data/d2c-e/testmini-*.parquet
  - config_name: dqa
    data_files:
      - split: test
        path: data/dqa/test-*.parquet
      - split: testmini
        path: data/dqa/testmini-*.parquet
dataset_info:
  - config_name: diagrams
    features:
      - name: diagram_id
        dtype: string
      - name: image
        dtype: image
      - name: source_code
        dtype: string
      - name: preamble
        dtype: string
      - name: domain
        dtype: string
    splits:
      - name: test
        num_examples: 3744
      - name: testmini
        num_examples: 300
  - config_name: d2c-p
    features:
      - name: id
        dtype: string
      - name: diagram_id
        dtype: string
      - name: image
        dtype: image
      - name: instruction
        dtype: string
      - name: code
        dtype: string
      - name: reference_image
        dtype: image
      - name: domain
        dtype: string
    splits:
      - name: test
        num_examples: 3739
      - name: testmini
        num_examples: 300
  - config_name: d2c-e
    features:
      - name: id
        dtype: string
      - name: diagram_id
        dtype: string
      - name: image
        dtype: image
      - name: instruction
        dtype: string
      - name: code
        dtype: string
      - name: reference_image
        dtype: image
      - name: domain
        dtype: string
    splits:
      - name: test
        num_examples: 7420
      - name: testmini
        num_examples: 600
  - config_name: dqa
    features:
      - name: id
        dtype: string
      - name: diagram_id
        dtype: string
      - name: image
        dtype: image
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: domain
        dtype: string
      - name: question_type
        dtype: string
      - name: output_instruction
        dtype: string
    splits:
      - name: test
        num_examples: 7146
      - name: testmini
        num_examples: 600

Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams

ECCV 2026

🏠 Homepage (coming soon) · 💻 Code · 📄 Paper (coming soon)

Diagram-MMU is a benchmark for evaluating Multimodal Large Language Models (MLLMs) on understanding, parsing, and editing scientific diagrams. It contains 3,744 curated diagrams (each with compilable source code) and 18,305 human-validated evaluation instances across six domains (charts, planar_geometry, 3d_shapes, graph_structures, chemistry, circuit_diagrams), over three tasks: diagram-to-code parsing (D2C-P), diagram-to-code editing (D2C-E), and diagram question answering (DQA).

Each config provides two splits: test (full benchmark) and testmini (a class-balanced 50-per-domain subset, 300 diagrams, for quick development). All ground truth is public, so evaluation runs locally with no submission.

Config	Task	#test	#testmini
diagrams	Canonical per-diagram source	3,744	300
d2c-p	Diagram-to-Code Parsing	3,739	300
d2c-e	Diagram-to-Code Editing	7,420	600
dqa	Diagram Question Answering	7,146	600

Data Distribution

Main Results

Usage

from datasets import load_dataset

d2cp = load_dataset("AIGrounding/Diagram-MMU", "d2c-p", split="test")       # parsing
dqa  = load_dataset("AIGrounding/Diagram-MMU", "dqa", split="testmini")     # QA, dev subset
diagrams = load_dataset("AIGrounding/Diagram-MMU", "diagrams", split="test")

ex = d2cp[0]
ex["image"]   # PIL.Image (decoded automatically)
ex["code"]    # ground-truth source

Evaluation

All ground truth is public, so evaluation runs locally — no submission. The official evaluation code (object / code / image metrics for D2C-P & D2C-E, and the rule-based + LLM-as-judge pipeline for DQA) will be released separately:

• Evaluation repository: github.com/AIGrounding/Diagram-MMU

License & Citation

Released under the Apache License 2.0. Source code is collected from official package handbooks (PGFPlots, CircuiTikZ, TKZ-Euclide, ChemFig, TikZ-Network) and community resources (texample.net, TeX Stack Exchange, GitHub tikz_favorites); upstream sources may carry their own terms. All annotations are original to this work.

@article{bo2026diagrammmu,
  title   = {Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams},
  author  = {Bo, Weihao and Zhang, Shan and Sun, Yanpeng and Liu, Jie and Yao, Yongke and Du, Jinhao and He, Wei and Zou, Kai and Li, Zechao and Wang, Jingdong},
  journal = {arXiv preprint arXiv:TODO},
  year    = {2026}
}