VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences

2026-08-21

AI Summary

论文介绍VIALS基准，用于评估AI对生命科学中视觉工件的解读能力，发现现有模型缺乏领域知识和特定视觉推理能力。

Abstract

In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ...) to inform research decisions. We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the types of artifacts examined throughout experimental workflows in the biotech industry (rather than polished figures from publications and textbooks). While frontier vision-language models can now fluently describe natural images, we find that they are unable to accurately interpret these scientific images, reflecting limitations in domain knowledge and domain-specific visual reasoning capabilities. In contrast, scientists with relevant domain expertise find these visual interpretation tasks straightforward. AI that cannot similarly interpret such images will have limited utility in professional life sciences workflows, where such artifacts are central to how scientists reason, communicate, and make decisions.

Problem

• visual artifacts
• life sciences
• vision-language models
• domain knowledge
• visual reasoning

Innovation

• VIALS
• visual question-answering
• life sciences artifacts
• domain-specific visual reasoning