Scientific Figure Understanding

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17 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

SciFigBench: A Benchmark for Scientific Method Figure Understanding
Oct 19, 2026Lijia Wu, Zheng Li, Yuxuan Liu +3Diagram Understanding Scientific Figure Understanding
VCIP, CS & AAIS, Nankai University · NKIARI, Shenzhen Futian · Li Auto.
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis
Oct 9, 2026Daniel J. Evans, J. P. Naiman, JooYoung Seo +1Scientific Figure Understanding Chart QA
School of Information Sciences, University of Illinois, Urbana-Champaign, 61820, USA
PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data
Oct 5, 2026Yaohui Zhang, Binxu Li, Haoyi Duan +7Scientific Visualization Chart QA
Generative Expert Labs, Inc · Stanford University · Princeton University
FigAct: Turning Scientific Figures into Active Canvases for Explanation
Sep 28, 2026Shishi Xiao, Zichao Wang, Alexa Siu +2Scientific Visualization Visually Grounded Reasoning
Brown University · Adobe Research
SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code
Sep 8, 2026Wentao Li, Yibo Wu, Yizhe Chen +5Scientific Visualization Code Generation Evaluation
Tsinghua University
Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding
Sep 7, 2026Alberlucia Rafael Soarez, Camila Ferreira, Daniel Kim +2Chart Understanding Chart QA
University of Brasilia
From Terminology to Diagrams: Visual-Instruction Generation for Scientific Diagram Understanding
Sep 1, 2026Raul Ortega, José Manuel Gómez-PérezSynthetic Data Generation for Language Models Synthetic Data Generation for VLMs
Language Technology Research Laboratory Expert.ai 17 Henri Dunant, 28036 Madrid, Spain
A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
Aug 14, 2026Jennifer D'Souza, Fahad Ahmed, Cecilia Andrea Bustamante Andrade +9Scientific Figure Understanding Multimodal Model Evaluation
Data Science and Digital Libraries, TIB Leibniz Information Centre for Science and Technology, Hannover, Germany · Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, Netherlands · Department of Biology, Chemistry, and Pharmacy, Freie Universität Berlin, Berlin, Germany +5
How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures
Aug 13, 2026Paul Osemudiame Oamen, Owusu-Banahene Osei, Ananya Mukherjee +4VLM Evaluation VLM Robustness
The Aberdeen NLP Research Group, University of Aberdeen, UK · International Institute of Information Technology Hyderabad, India · University of Technology Nuremberg, Germany +1
SciFigPlag-Bench: A Benchmark for Provenance-Aware Scientific Figure Plagiarism Detection
Jul 31, 2026Zhiying Cui, Minghao Yang, Linlin Gao +2VLM Evaluation Image Forgery Detection
1Ningbo University · 2Hokkaido University · 3IBM Research
SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence
Jul 29, 2026Chuanzhi Xu, Zihan Deng, Huiqi Liang +4Scholarly Peer Review Image Quality Assessment
The University of Sydney · The University of Hong Kong · Indiana University +2
ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching (ALD/E) Scientific Figures
Jul 29, 2026Fahad Ahmed, Sören Auer, Jennifer D'SouzaVisual Question Answering Document Information Extraction
TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany · L3S Research Center, Leibniz University, Hannover, Germany
Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature
Jun 29, 2026Subham Ghosh, Shubham Tiwari, Mohammad Ibrahim +1LLM-Assisted Annotation Materials Science
Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee, Uttarakhand, India-247667 · Department of Metallurgical and Materials Engineering Indian Institute of Technology Roorkee, Uttarakhand, India-247667
Helping Figures Tell their Story! Paper-Grounded Video Generation Explaining Complex Scientific Figures
Jun 10, 2026Ishani Mondal, Javad Baghirov, Jordan Boyd-GraberImage-to-Video Generation Video Generation
University of Maryland, College Park
PlotPick: AI-powered batch extraction of numerical data from scientific figures
May 7, 2026Tommy CarstensenData Visualization Vision-Language Models
Copenhagen Research Centre for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning
Apr 30, 2026Junpeng Ding, Zichen Tang, Haihong E +17AI for Science VLM Reasoning
Beijing University of Posts and Telecommunications
Anagent For Enhancing Scientific Table & Figure Analysis
Feb 10, 2026Xuehang Guo, Zhiyong Lu, Tom Hope +1Scientific Figure Understanding AI for Science
College of William & Mary · NIH - National Library of Medicine · The Allen Institute for AI (AI2)

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SciFigBench: A Benchmark for Scientific Method Figure Understanding

Lijia Wu, Zheng Li, Yuxuan Liu, Jingwen Zhang+2

VCIP, CS & AAIS, Nankai University · NKIARI, Shenzhen Futian · Li Auto.

Diagram Understanding Scientific Figure Understanding VLM Evaluation Visual Reasoning

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Scientific method figures often provide researchers with a clear visual entry point into a paper's methodology and core contributions, yet their dense layouts and complex information flows are difficult for Vision Language Models (VLMs) to interpret. Existing benchmarks predominantly focus on coarse-grained tasks, such as context-based retrieval or holistic comprehension, overlooking the fine-grained content and inter-module relations within complex methodological diagrams. To address these overlooked aspects, we introduce Scientific Figure Benchmark (SciFigBench), a benchmark for evaluating core capabilities required for scientific method figure understanding. Specifically, it targets three capabilities: dense content perception, visual relation comprehension, and information-flow tracing. For this purpose, we curate 4,352 scientific method figures from real-world research papers and construct 40,670 QA pairs to operationalize these capabilities as measurable question-answering tasks. All annotations are generated by dedicated pipelines and then manually verified. We further propose SciFigCue, a training-free visual prompting module that externalizes arrow-induced dependencies as visual relational evidence for method-figure reasoning. Experiments across open-source and proprietary VLMs show that even strong models remain substantially limited in visual relation comprehension, while SciFigCue improves performance across several tasks without parameter updates. The code and related resources are available at https://github.com/LeNcyer/SciFigBench.