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PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- PathAgentBench benchmark evaluates vision-language models on whole-slide pathology images across evidence-seeking tasks using 1,822 annotated TCGA slides.
- Why it matters
- Matters for engineers building AI systems for digital pathology who need to assess model performance on realistic gigapixel image analysis workflows.
- Watch out
- Models excel at reasoning over curated evidence but struggle with finding relevant regions directly in slides, with hit rates dropping sharply at higher magnifications.
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