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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T SciFigBench: A Benchmark for Scientific Method Figure Understanding
%A Wu, Lijia
%A Li, Zheng
%A Liu, Yuxuan
%A Zhang, Jingwen
%A Zhao, Penghai
%A Li, Xiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-scifigbench
%X 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.
%U https://aclanthology.org/2026.emnlp-main.642/
%P 14776-14800

Markdown (Informal)

[SciFigBench: A Benchmark for Scientific Method Figure Understanding](https://aclanthology.org/2026.emnlp-main.642/) (Wu et al., EMNLP 2026)

• SciFigBench: A Benchmark for Scientific Method Figure Understanding (Wu et al., EMNLP 2026)

ACL

• Lijia Wu, Zheng Li, Yuxuan Liu, Jingwen Zhang, Penghai Zhao, and Xiang Li. 2026. SciFigBench: A Benchmark for Scientific Method Figure Understanding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14776–14800, Budapest, Hungary. Association for Computational Linguistics.