SciVer: Evaluating Foundation Models for Multimodal Scientific Claim
  Verification
Authors: name: Chengye Wang, name: Yifei Shen, name: Zexi Kuang, name: Arman Cohan, name: Yilun Zhao

We introduce SciVer, the first benchmark specifically designed to evaluate
the ability of foundation models to verify claims within a multimodal
scientific context. SciVer consists of 3,000 expert-annotated examples over
1,113 scientific papers, covering four subsets, each representing a common
reasoning type in multimodal scientific claim verification. To enable
fine-grained evaluation, each example includes expert-annotated supporting
evidence. We assess the performance of 21 state-of-the-art multimodal
foundation models, including o4-mini, Gemini-2.5-Flash, Llama-3.2-Vision, and
Qwen2.5-VL. Our experiment reveals a substantial performance gap between these
models and human experts on SciVer. Through an in-depth analysis of
retrieval-augmented generation (RAG), and human-conducted error evaluations, we
identify critical limitations in current open-source models, offering key
insights to advance models' comprehension and reasoning in multimodal
scientific literature tasks.