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What Language Adds, and at What Cost: Four Controlled Probes of Pathology Vision-Language Models

Wenhao Zhang, Zhongliang Zhou, Yi Ping, Zirui Wang, Sheng Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26805 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

视觉 - 语言导航模型(VLMs)承诺使计算病理可查,并能够起草报告,但语言增加了多少超越视觉脊椎是不清楚的,因为几乎每个系统是由其开发者评估的测试集自己的选择。我们评级发布的证据是如何产生的,并测试了四个声明与控制的探测器,重复使用释放的检查点和公共数据,不同一个输入一次。报告文本添加预测信号:添加一个冻结嵌入的阶段编辑的报告到一个幻灯片嵌入增加了汇总的C指数为0.019的进展自由间隔和0.016的总生存在1{33}6TCGA患者,并恢复了阶段声明添加了。 Prompts没有添加任何可测量的准确性

Machine translation (Meta M2M100); the English original follows.

Abstract

Vision--language models (VLMs) promise to make computational pathology queryable and able to draft reports, but how much language adds beyond the vision backbone is unclear, because almost every system is evaluated by its developers on test sets of their own choosing. We grade published evidence by how it was produced and test four claims with controlled probes that reuse released checkpoints and public data, varying one input at a time. Report text adds prognostic signal: adding a frozen embedding of the stage-redacted report to a slide embedding raises the pooled C-index by 0.019 for progression-free interval and by 0.016 for overall survival across 1{,}633 TCGA patients, and restoring the stage statement adds nothing. Prompts add no measurable accuracy at inference: with features and pooling fixed, zero-shot classification is statistically indistinguishable from a supervised linear probe for lung and renal cancer subtyping. Slide-level question answering is barely grounded: SlideChat answers 74.82\% of SlideBench-VQA(TCGA) items correctly with the slide and 74.41\% without it, whereas its language backbone alone reaches 43.52\%. Paraphrasing leaves accuracy unchanged but flips 8--23\% of answers. Self-reported gains from agentic navigation and few-shot language priors cannot yet be attributed to language. Language thus adds real but narrow value, and we propose minimum controls that would expose these failure modes in future evaluations.

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Topics: Pooling, Computer science, Natural language processing, Reuse, Embedding, Artificial intelligence

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