13 papers
Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Ziyi Wang, Xumin Yu, Yongming Rao +19
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situatio…
Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners
Zheng Lu, Mingqi Gao, Qinlei Xie +8
Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. This rewards models that mimic…
Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding
Chang Liu, Henghui Ding, Nikhila Ravi +40
This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, whi…
Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment
Jingkun Chen, Ruoshi Xu, Mingqi Gao +2
Point-Vision-Language Models promise to empower embodied agents with executable spatial reasoning, yet they frequently succumb to geometric hallucination where predicted 3D structu…
UniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation
Haofeng Liu, Ziyue Wang, Alex Y. W. Kong +6
Surgical video segmentation is fundamental to computer-assisted surgery. In practice, surgeons need to dynamically specify targets throughout extended procedures, using heterogeneo…
Re-Prompting SAM 3 via Object Retrieval: 3rd of the 5th PVUW MOSE Track
Mingqi Gao, Sijie Li, Jungong Han
This technical report explores the MOSEv2 track of the PVUW 2026 Challenge, which targets complex semi-supervised video object segmentation. Built on SAM~3, we develop an automatic…