@inproceedings{xu-etal-2026-reactbench,
title = "{R}eact{B}ench: A Benchmark for Topological Reasoning in {MLLM}s on Chemical Reaction Diagrams",
author = "Xu, Qiang and
Bai, Shengyuan and
Wang, Yu and
Cao, He and
Leqingchen and
Liu, Yuanyuan and
Feng, Bin and
Liu, Zijing and
Li, Yu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.597/",
pages = "13744--13762",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced with complex topological structures involving branching paths, converging flows, and cyclic dependencies, their reasoning capabilities degrade sharply, even on tasks as basic as counting endpoints. Existing benchmarks fail to probe this gap, focusing on semantic comprehension rather than structural reasoning. We introduce **ReactBench**, a benchmark that reveals fundamental limitations in structural reasoning through chemical reaction diagrams. These real-world scientific diagrams offer an ideal testbed because they naturally span diverse structures from linear chains to cyclic graphs, while requiring both precise local recognition and coherent global reasoning. Our benchmark comprises 1,618 expert-annotated QA pairs across four hierarchical task dimensions. Extensive evaluation across 24 MLLMs reveals a significant performance gap exceeding 30{\%} between anchor-based tasks and holistic structural reasoning tasks."
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%0 Conference Proceedings
%T ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams
%A Xu, Qiang
%A Bai, Shengyuan
%A Wang, Yu
%A Cao, He
%A Liu, Yuanyuan
%A Feng, Bin
%A Liu, Zijing
%A Li, Yu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Leqingchen
%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 xu-etal-2026-reactbench
%X Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced with complex topological structures involving branching paths, converging flows, and cyclic dependencies, their reasoning capabilities degrade sharply, even on tasks as basic as counting endpoints. Existing benchmarks fail to probe this gap, focusing on semantic comprehension rather than structural reasoning. We introduce **ReactBench**, a benchmark that reveals fundamental limitations in structural reasoning through chemical reaction diagrams. These real-world scientific diagrams offer an ideal testbed because they naturally span diverse structures from linear chains to cyclic graphs, while requiring both precise local recognition and coherent global reasoning. Our benchmark comprises 1,618 expert-annotated QA pairs across four hierarchical task dimensions. Extensive evaluation across 24 MLLMs reveals a significant performance gap exceeding 30% between anchor-based tasks and holistic structural reasoning tasks.
%U https://aclanthology.org/2026.emnlp-main.597/
%P 13744-13762
Markdown (Informal)
[ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams](https://aclanthology.org/2026.emnlp-main.597/) (Xu et al., EMNLP 2026)
ACL
- Qiang Xu, Shengyuan Bai, Yu Wang, He Cao, Leqingchen, Yuanyuan Liu, Bin Feng, Zijing Liu, and Yu Li. 2026. ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13744–13762, Budapest, Hungary. Association for Computational Linguistics.