PhysVista is a new benchmark that evaluates physical intelligence in Vision‑Language Models (VLMs) by integrating perception, reasoning, and plausibility assessment into a closed cognitive loop. It distinguishes between event‑level and scale‑level reasoning and tests models on both real‑world and AI‑generated videos to provide a holistic, fine‑grained analysis of physical understanding. Experiments show significant gaps in VLMs’ physical reasoning and plausibility assessment, underscoring the need for more principled, physically grounded multimodal designs.
By Xinge Peng, Yiting Lu, Tianwu Zhi, Wen Wen, Jianzhao Liu, Xin Li, Zhibo Chen
arXiv:2608. 02150v2 Announce Type: replace-cross Abstract: Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities.
By Zhongjie Ba, Shengwang Xu, Peng Cheng, Jinyang Zou, Ting Yu, Zhibo Wang, Zhan Qin
arXiv:2605.04515v2 Announce Type: replace
Abstract: Video Large Language Models (Video-LLMs) excel in general video understanding but often base physical judgments on event expectations rather than o...
By Zicheng Zhao, Chaofan Gan, Shijie Li, Weiyao Lin
Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on general video understanding tasks, current video-language models still struggle to reliably determine whether an observed event conforms to specific physical laws.
arXiv:2606. 05966v1 Announce Type: cross Abstract: Understanding and reasoning about the physical world is the foundation of intelligent behavior, yet state-of-the-art vision-language models (VLMs) still fail at causal physical reasoning, often producing plausible but incorrect answers.
By Tianyi Tang, Zhuoyi Lin, Zeyu Feng, Tianyi Ma, Yew-Soon Ong, Ivor Tsang, Haiyan Yin
arXiv:2608. 04575v1 Announce Type: cross Abstract: Reliable physical reasoning from video requires understanding how objects move, interact, and respond to interventions.
By Chen Yang, Shenxiang Zeng, Haoyang Zhao, Zhouyuan Xu, Youquan He, Haoyu Li, Mingyi Deng, Jiansheng Fan, Chen Wang
arXiv:2509. 12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge.
By Gautam Sreekumar, Vishnu Naresh Boddeti
The paper introduces Physically Plausible Video Generation (PPVG), a method that generates videos consistent with physical laws by treating physical evolution as a chain of causally connected events. It employs three modules: Physics-driven Event Chain Reasoning to decompose phenomena into scene-graph events, Transition-aware Routed Keyframe Conditioning to guide keyframe synthesis for smooth transitions, and Physics-injected Contrastive Semantic Guidance to steer generation toward plausible dynamics. Experiments on multiple physics benchmarks show improved physical plausibility compared to prior approaches.
By Zixuan Wang, Yixin Hu, Wen Li, Feng Chen, Yan Liu, Duo Peng, Yinjie Lei
arXiv:2508. 10956v3 Announce Type: replace-cross Abstract: Inspired by human categorization, visual reasoning about object properties, such as physical attributes and functions, involves identifying and recognizing low-level details and higher-level abstractions.
By Abhishek Kolari, Mohammadhossein Khojasteh, Yifan Jiang, Floris den Hengst, Filip Ilievski
VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.
By Junxiang Xu, Ruisi Wang, Fanyi Pu, Maijunxian Wang, Ran Ji, Tongxi Zhou, Chenyang Gu, Jing Zuo, Hongcan Xiao, Yimeng Geng, Wanqi Yin, Wei Chen, Oscar Qian, Zhengan Yan, Ziqi Huang, Haiwen Diao, Liang Pan, Bo Li, Xiangyu Fan, Dezhi Luo, Fengyuan Yu, Zehong Zhao, Qingying Gao, Tinghui Zhu, Yilan Zhang, Jingqi Tong, Pinyuan Feng, Zhengze Jiang, Letian Wang, Ziyu Guo, Renrui Zhang, Jieneng Chen, Sonia Joseph, Constantin Venhoff, Saman Motamed, Mengyue Yang, Chandra Sripada, Alan Yuille, Philip Torr, Lvmin Zhang, Vikash Kumar, Daniel Khashabi, Nikolaus Kriegeskorte, Rapha\"el Milli\`ere, Vincent C. M\"uller, Anyi Rao, Quan Wang, Ziwei Liu, Dahua Lin, Lei Yang, Hokin Deng, Zhongang Cai
EgoMemReason is a new benchmark for week‑long egocentric video understanding that focuses on memory‑driven reasoning rather than simple perception tasks. It tests three memory types—entity, event, and behavior—across 500 questions, each requiring evidence from an average of 5.1 video segments and 25.9 hours of backtracking. Evaluation of 17 models shows that even the best achieves only 39.6% accuracy, highlighting the difficulty of long‑horizon memory in multimodal systems.
By Ziyang Wang, Yue Zhang, Shoubin Yu, Ce Zhang, Zengqi Zhao, Jaehong Yoon, Hyunji Lee, Gedas Bertasius, Mohit Bansal
arXiv:2610.01162v1 Announce Type: new
Abstract: Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot video...
By Isaiah Milkey, Som Sagar, Aditya Taparia, Xinyuan Liu, Jiqing Wen, Ransalu Senanayake