SlideBank is a training‑free framework that turns each whole‑slide image into a persistent, concept‑indexed evidence bank. It performs coarse‑to‑fine exploration to locate informative regions and multi‑scale views, converts them into explicit morphological observations, and anchors pathology signals to the supporting patches and slide coordinates. During inference, questions are routed to relevant signals and evidence scales, and a confidence‑based cross‑level consensus integrates global, regional, and patch evidence, achieving high accuracy on WSI‑VQA and SlideBench‑BCNB while enabling consistent re‑phrasing and reduced inference cost.
By Beidi Zhao, Gexin Huang, Ciro Zhang, Anqi Li, Yusheng Tan, Chen Zhou, Gang Wang, Zu-hua Gao, Xiaoxiao Li
Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most existing pathology benchmarks evaluate models on pre-cropped patches or pre-extracted slide features, leaving their ability to acquire evidence directly from gigapixel WSIs largely untested.
arXiv:2608. 10827v1 Announce Type: cross Abstract: Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence.
By Shengzhi Wang, Jun Yang, Kai Wu, Xiaozhong Ji, Yiwen Ye, Ziyang Chen, Mingliang Xiong, Wen Fang, Mingqing Liu, Mengyuan Xu, Miaoxuan Shan, Caiyan Liu, Bin He, Qingwen Liu
arXiv:2607. 19261v1 Announce Type: cross Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence.
By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin
arXiv:2607.19261v4 Announce Type: replace-cross
Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating...
By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin
arXiv:2608. 14015v1 Announce Type: cross Abstract: Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a procedure by grounding the question in visual evidence spread across time.
By Yingying Fan, Penghui Du, Leyan Zhu, Runze He, Zimeng Wu, Yuxuan Zhang, Liang Chen, Jiahao Xie, Jiangtang Wang, Shuai Shao, Anchao Yang, Yutong Bai, Yan Wang
arXiv:2606. 07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging.
By Chengyang Zhang, Wenchuan Zhang, Bo Li, Mengran Li, Bob Zhang, Yuhao Yi, Hong Bu, Jiancheng Lv
arXiv:2508.07292v4 Announce Type: replace-cross
Abstract: Endoscopic diagnosis is an iterative process in which clinicians acquire, compare, and verify local visual evidence before reaching a conclus...
By Yi Tang, Kai-Ni Wang, Liang-Peng Pu, Hui Tang, Xiaopu He, Guang-Quan Zhou
arXiv:2609.39566v1 Announce Type: new
Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...
By Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long
arXiv:2512. 05774v2 Announce Type: replace-cross Abstract: Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrelevant content.
By Ziyang Wang, Honglu Zhou, Shijie Wang, Junnan Li, Caiming Xiong, Silvio Savarese, Mohit Bansal, Michael S. Ryoo, Juan Carlos Niebles
The paper introduces PACE, a factor-guided, progressive framework for acquiring evidence in long-video question answering. PACE first indexes clip-level descriptions using question-derived factors, then refines evidence retrieval with contrastive cues derived from candidate answers. On the MMR‑V dataset, PACE achieves 42.6% accuracy and recovers 66.9% of annotated cues, outperforming direct inference and prior agentic baselines, and shows consistent improvements across several long-video benchmarks.
By Baixuan Xu, Yinyui Xu, Tianshi Zheng, Zhaowei Wang, Weiqi Wang, Haochen Shi, Jiayu Liu, Qing Zong, Xiyu Ren, Xinyu Geng, Zhitao He, Yangqiu Song
PathPocket is a multimodal AI co‑pilot that grounds pathology decision‑making in evidence. It builds the largest pathology evidence corpus (≈110,472 documents) and a hypergraph of 4.55 million entities and 7.10 million relations to support traceable reasoning. The system handles text and multimodal queries, including ROI and gigapixel whole‑slide images, and outperforms current state‑of‑the‑art models on a benchmark of over 200,000 real‑world cases, improving pathologists’ diagnostic accuracy and confidence.
By Zhe Xu, Zhengyu Zhang, Zhiyuan Cai, Jiahao Xu, Yijie Lin, Ziyi Liu, Junlin Hou, Hongyi Wang, Yuxiang Nie, Yihui Wang, Jiabo Ma, Ling Liang, Yingxue Xu, Zhengrui Guo, Guanghao Wu, Danyi Li, Ziqi Zhou, Donglin Tan, Zhijian Cen, Ying Tan, Xiaolin Liu, Qi Xie, Xiaoying Tang, Xi Peng, Cheng Deng, Lijuan Qu, Ronald Cheong Kin Chan, Li Liang, Hao Chen