AGENTIC-SYSTEMSApril 20, 20261 min read9 views

ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams

ReactBench is a new benchmark designed to evaluate the topological reasoning capabilities of Multimodal Large Language Models (MLLMs) using chemical reaction diagrams. The study reveals that current AI still faces significant challenges in understanding complex geometric structures, highlighting a critical bottleneck in visual reasoning.

Computer Science > Artificial Intelligence

Title:ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams

View PDF HTML (experimental)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 17 MLLMs reveals a significant performance gap exceeding 30% between anchor-based tasks and holistic structural reasoning tasks. Controlled ablations confirm this bottleneck lies in reasoning, not perception. These findings expose a fundamental deficit in structural understanding and establish directions for advancing visual reasoning.

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Source: arXiv cs.AI Recent

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