This work introduces SurgCoTBench, the first reasoning-focused benchmark in RAS, and proposes SurgRAW, a clinically aligned Chain-of-Thought (CoT) driven agentic workflow for zero-shot multi-task reasoning in surgery, which surpasses mainstream VLMs and agentic systems and outperforms a supervised model.
Chang Han Low, Ziyue Wang, Tianyi Zhang et al.· IEEE Robotics and Automation...· 20 citations· ⚡2
This study establishes pathoentity shuffling as an effective principle for pathological image analysis, bridging biological insight and computational design to enhance diagnostic modeling and morphological hierarchy learning.
Zeyu Liu, Tianyi Zhang, Brian K. Chen et al.· npj Biomedical Innovations· 0 citations
SlideMix is proposed, a model-agnostic multimodal augmentation framework for MIL-based WSI analysis that improves accuracy and generalization in most settings and compares favorably with established augmentation baselines, providing a simple plug-and-play approach for more robust and scalable digital pathology models.
Chad Wong, Si-Cheng Chen, Tian-Yi Zhang et al.· 0 citations
PathA-gentBench is introduced, a benchmark for evaluating evidence-seeking vision-language models (VLMs) across four complementary capabilities: image-to-text matching for evidence interpretation, text-to-image retrieval for evidence verification, diagnostic-region localization for evidence acquisition, and multi-scale...
A benchmark and training framework for shortcut-resistant cross-scale pathology reasoning, and an Adversarial Text-only Screening strategy for semantic reasoning questions and a Structure-controlled Distractor Sampling strategy for visual grounding questions, encouraging models to rely on cross-scale visual evidence.
Chi Phan, Tian-Yi Zhang, Yu-Feng Wu et al.· arXiv.org· 1 citation
EviPathBench is introduced, a benchmark for evaluating evidence acquisition and reasoning in vision-language models (VLMs) for whole-slide pathology and reveals a pronounced gap between reasoning over curated evidence and acquiring it from WSIs.
Dan-Kai Liao, Tian-Yi Zhang, Yu-Feng Wu et al.· 3 citations
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