ChemVTS-Bench is a domain-authentic benchmark that evaluates Visual‑Textual‑Symbolic reasoning in multimodal large language models for chemistry. It presents diverse chemical problems—organic molecules, inorganic materials, and 3D crystal structures—in three input modes: visual-only, visual‑text hybrid, and SMILES-based symbolic. The benchmark includes an automated agent workflow for inference, answer verification, and failure diagnosis, and shows that visual-only inputs and structural chemistry remain challenging for current models.
By Zhiyuan Huang, Baichuan Yang, Zikun He, Yanhong Wu, Fang Hongyu, Zhenhe Liu, Lin Dongsheng, Bing Su
oMeBench is a large-scale, expert-curated benchmark designed to evaluate large language models (LLMs) on organic mechanism reasoning. It contains over 10,000 annotated mechanistic steps, including reaction type labels, intermediate structures, and difficulty ratings, and introduces the oMeS scoring framework to assess logical consistency and chemical structural similarity. Evaluation shows that while current LLMs display promising chemical intuition, they often fail to produce correct and consistent multi-step reasoning, though prompting and fine-tuning can bring smaller models up to the level of closed‑source frontier models.
By Ruiling Xu, Yifan Zhang
arXiv:2607. 14658v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning.
By Mingze Xu, Yinghui Li, Jiayi Kuang, Zhanhui Kang, Di Yin, Ying Shen, Xing Sun, Yuxing Han
arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.
By Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You
RxnExBench is a multimodal benchmark designed to evaluate vision‑language models on the task of extracting structured chemical reaction data from dense figures in synthetic chemistry literature. The dataset, compiled from high‑impact journals, contains 3,565 expert‑annotated reactions and 2,361 molecular structures, organized under a unified schema of 15 reaction roles and 26 core fields. It also introduces a difficulty taxonomy, a modular annotation pipeline, and the Oracle‑MolRec protocol to separate molecular recognition from higher‑level reasoning, and shows that current state‑of‑the‑art models struggle as schema complexity and reasoning demands increase.
By Jiaxing Sun, Junyuan Gao, Ruikang Zhang, Hao Zheng, Lingli Ge, Xiao An, Jiang Wu, Huazheng Zeng, Jiahe Song, Chen Zhu, Haote Yang, Jingchao Wang, Lu Chen, Yuanyuan Cao, Ruijie Zhang, Shengxin Lu, Weijia Li, Lijun Wu, Conghui He
arXiv:2607. 03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding.
By Wenda Wang, Jinjia Feng, Zhewei Wei
The paper introduces a framework that combines a Geometric Vision Parser and a Symbolic Solver to enable a Large Language Model to solve complex plane geometry problems. By translating diagrams into symbolic representations and performing formal deductions, the approach reduces hallucinations and produces interpretable, human-like solutions. Experiments on a new benchmark from 2025 Chinese Zhongkao exams show performance comparable to Gemini 2.5 Pro.
By Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou, Yan Cao, Xin Shen, Dongcai Lu, Yi Zhou
Local chemical perception and property reasoning are both essential for understanding how molecular structure determines properties. Current LLM-based chemical reasoning methods either receive SMILES/molecular images together with descriptions of local motifs, or reason directly from molecular images.
arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.
By Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai
The survey titled "When Vision Meets Graphs: A Survey on Graph Reasoning and Learning" reviews how visual depictions of graphs can be used as inputs for graph reasoning and learning. It highlights that while Graph Neural Networks dominate graph machine learning, most pipelines ignore the visual form of graphs, despite scientists routinely interpreting graphs visually. The paper organizes existing work into three threads—vision for graph reasoning, vision for graph learning, and scientific graphs—aiming to clarify current capabilities and chart a path toward foundation models that perceive and reason about graphs like scientists do.
By Xinjian Zhao, Wei Pang, Zhixuan Yu, Xiangru Jian, Xiaozhuang Song, Yaoyao Xu, Zhongkai Xue, Dingshuo Chen, Shu Wu, Philip Torr, Tianshu Yu
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order.