From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models
This paper introduces the concept of Microscopic Spatial Intelligence (MiSI), the capability to perceive and reason about the spatial relationships of invisible microscopic entities, which is fundamental to scientific discovery. To assess the potential of Vision-Language Models (VLMs) in this domain, we propose a systematic benchmark framework MiSI-Bench. This framework features over 163,000 question-answer pairs and 587,000 images derived from approximately 4,000 molecular structures, covering nine complementary tasks that evaluate abilities ranging from elementary spatial transformations to complex relational identifications. Experimental results reveal that current state-of-the-art VLMs perform significantly below human level on this benchmark. However, a fine-tuned 7B model demonstrates substantial potential, even surpassing humans in spatial transformation tasks, while its poor performance in scientifically-grounded tasks like hydrogen bond recognition underscores the necessity of integrating explicit domain knowledge for progress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.
693bb6089874a2a5e4ffb39e
Zongzhao Li
claimed_verified
2025-12-12T09:14:42.257Z
670f86e4d75f114352916a35
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/g3zZ6TTbgV-Xu789lPaYN.png
Li
zongzhao
user
zongzhao
693bb6089874a2a5e4ffb39f
Xiangzhe Kong
693bb6089874a2a5e4ffb3a0
Jiahui Su
693bb6089874a2a5e4ffb3a1
Zongyang Ma
693bb6089874a2a5e4ffb3a2
Mingze Li
693bb6089874a2a5e4ffb3a3
Songyou Li
693bb6089874a2a5e4ffb3a4
Yuelin Zhang
693bb6089874a2a5e4ffb3a5
Yu Rong
693bb6089874a2a5e4ffb3a6
Tingyang Xu
693bb6089874a2a5e4ffb3a7
Deli Zhao
693bb6089874a2a5e4ffb3a8
Wenbing Huang
From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models
This paper introduces the concept of Microscopic Spatial Intelligence (MiSI), the capability to perceive and reason about the spatial relationships of invisible microscopic entities, which is fundamental to scientific discovery. To assess the potential of Vision-Language Models (VLMs) in this domain, we propose a systematic benchmark framework MiSI-Bench. This framework features over 163,000 question-answer pairs and 587,000 images derived from approximately 4,000 molecular structures, covering nine complementary tasks that evaluate abilities ranging from elementary spatial transformations to complex relational identifications. Experimental results reveal that current state-of-the-art VLMs perform significantly below human level on this benchmark. However, a fine-tuned 7B model demonstrates substantial potential, even surpassing humans in spatial transformation tasks, while its poor performance in scientifically-grounded tasks like hydrogen bond recognition underscores the necessity of integrating explicit domain knowledge for progress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.
A benchmark framework evaluates Vision-Language Models in understanding microscopic spatial relationships, showing potential but highlighting the need for domain-specific knowledge integration.
Vision-Language Models VLMs Microscopic Spatial Intelligence MiSI MiSI-Bench spatial transformations relational identifications hydrogen bond recognition scientific AGI