Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning

TL;DR

SPUR is a new benchmark for scientific image perception and reasoning, testing models on fine-grained perception, cross-panel relations, and expert-level inference with over 4,200 QA pairs.

Contribution

Introduces SPUR, a comprehensive benchmark with novel evaluation dimensions for scientific image understanding and reasoning in multimodal large language models.

Findings

Current models significantly underperform compared to human experts.

Evaluation reveals critical bottlenecks in AI for Science research.

20 MLLMs and 4 Chain-of-Thought methods were comprehensively tested.

Abstract

We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. SPUR features three key innovations: (1) Panel-Level Fine-Grained Perception: evaluating the visual perception of multimodal large language models (MLLMs) across three dimensions (numerical, morphological, and information localization) on six fine-grained panel types; (2) Cross-Panel Relation Understanding: utilizing complex images with an average of 14.3 panels per sample to evaluate MLLMs' ability to decipher intricate cross-panel relations; (3) Expert-Level Reasoning: assessment of qualitative and quantitative reasoning across five experimental paradigms to determine if models can infer conclusions from evidence as human experts do. Comprehensive evaluation of 20 MLLMs and four multimodal Chain-of-Thought (MCoT) methods reveals that current models fall significantly short of the expert-level requirements for scientific image interpretation, underscoring a critical bottleneck in AI for Science (AI4S) research.