From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models

Abstract

This paper introduces the concept of Microscopic Spatial In-telligence (MiSI), the capability to perceive and reason about the spatialrelationships of invisible microscopic entities, which is fundamental toscientific discovery. To assess the potential of Vision-Language Models(VLMs) in this domain, we propose a systematic benchmark frameworkMiSI-Bench. This framework features over 163,000 question-answer pairsand 587,975 images derived from approximately 4,000 molecular struc-tures, covering nine complementary tasks that evaluate abilities rangingfrom elementary spatial transformations to complex relational identifi-cations. Experimental results reveal that current state-of-the-art VLMsperform significantly below human level on this benchmark. However,a fine-tuned 7B model demonstrates substantial potential, even sur-passing humans in spatial transformation tasks, while its poor perfor-mance in scientifically-grounded tasks like hydrogen bond recognitionunderscores the necessity of integrating explicit domain knowledge forprogress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.