arXiv:2607. 17834v1 Announce Type: cross Abstract: Endoscopic visual question answering (VQA) increasingly asks complex questions that combine several endoscopic answer components rather than isolated factual queries.
By Yuhao Liu, Cheng Zhao, Guanghui Yue
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:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.
By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi
arXiv:2609.24799v1 Announce Type: new
Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generi...
By Yeji Kim, Mi-Young Kim, Randy Goebel
arXiv:2610.10156v1 Announce Type: new
Abstract: Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, exi...
By Leon Mayer, Lucas Luttner, Patrick Godau, Kai Fritzsche, Annika Reinke, Leonie Boland, Jule Brandt, Janne Heinecke, Chloe K. Nobuhara, Niklas Holzwarth, Evangelia Christodoulou, Marcel Knopp, Dominik Michael, Pascale Piermarco, Saliq Neyaz, Korhan Derin \"Ozarslan, Jakob Hennighausen, Carlos Aumente-Maestro, Tim R\"adsch, Dheeraj Baji, Peter Maximilian Full, Finn Aichholz, Justus Veit Erpenbeck, Linus Finn Schott, Bastian Winkelhausen, Claas de Boer, Bianca G\"uttner, Anneli Hummel, Gregor Just, Max Kirchner, Chenyang Li, Rozenn Raffaut, Ariel Rodriguez, Danush Kumar Venkatesh, Kevin Wang, Jinjing Xu, Mona Sheikh Zeinoddin, Salman Khan, Thomas M. Pausch, Stefanie Speidel, Danail Stoyanov, Daniel A. Hashimoto, Fiona R. Kolbinger, Thomas G. Weiser, Lena Maier-Hein
The paper introduces SCoRE, an agentic framework for Visual Retrieval-Augmented Generation that explicitly selects and consolidates visual evidence before generating answers. It addresses two key challenges: sparse, scattered evidence and noisy exploration trajectories that obscure reasoning. By maintaining a textual ledger of relevant observations and reloading original images for a logical evidence sequence, SCoRE decouples reasoning from exploration and enforces strict visual grounding, with training that rewards evidence coverage, compactness, and answer correctness.
By Yucheng Shen, Lingyong Yan, Jiulong Wu, Shuaiqiang Wang, Jianmin WU, Dawei Yin, Min Cao