Do Pathology Vision-Language Models Truly See Pathology?

Chengyang Zhang; Wenchuan Zhang; Bo Li; Xinyu Liu; Jiaming Yang; Mengran Li; Chenxun Deng; Jie Chen; Yang Zhang; Wei Ju; Yuhao Yi; Hong Bu; Jiancheng Lv

arXiv · 2607.21065

Do Pathology Vision-Language Models Truly See Pathology?

Chengyang Zhang·Wenchuan Zhang·Bo Li·Xinyu Liu·Jiaming Yang·Mengran Li·Chenxun Deng·Jie Chen·Yang Zhang·Wei Ju·Yuhao Yi·Hong Bu·Jiancheng Lv

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Abstract

Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. However, we dig into current evaluations and identify three overlooked issues: 1) Visual evidence is not always necessary. For instance, Gemini-3-Pro achieves 53.5% average accuracy across 5 VQA benchmarks without any visual input. 2) Domain training can improve accuracy without proportional gains in visual binding. Compared with Qwen2.5-VL-7B, Patho-R1-7B exhibits a 5.8-point lower multimodal gain and a 3.7-point lower attention IoU. 3) Entity-level attention is diffuse and weakly query-specific. On PathVG, attention maps remain highly correlated across different entity queries. These issues can lead to substantial misjudgments of pathology VLMs' actual multimodal capabilities. To this end, we present PathBind, a benchmark comprising 2,600 samples: PathBind-VQA with 1,500 questions across six dimensions, PathBind-PTA with 600 questions from a private pathology teaching atlas, and PathBind-Grounding with 500 expert-curated region-level samples. Each component undergoes task-specific automated filtering and expert review to reduce textual shortcuts and improve entity-region correspondence. We evaluate 18 representative VLMs on VQA samples of PathBind and five existing pathology VQA benchmarks, and further evaluate 10 VLMs on PathBind-Grounding and PathVG. Results show that current pathology VLMs still exhibit a substantial gap between answer-side performance and visual-semantic binding.

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BibTeX RIS

Chengyang Zhang, Wenchuan Zhang, Bo Li, Xinyu Liu, Jiaming Yang, Mengran Li, Chenxun Deng, Jie Chen, Yang Zhang, Wei Ju, Yuhao Yi, Hong Bu, Jiancheng Lv. 2026-07-23. Do Pathology Vision-Language Models Truly See Pathology?. https://arxiv.org/abs/2607.21065

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