PhysicsLENS: Diagnosing Physical Property Blindness in Video Generation Models

arXiv:2610.01162 · cs.CV, cs.RO · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/huggingface/lerobot

Terminology

• Benchmark — 31× in this paper · explained in 2 episodes
• AUC — 19× in this paper · explained in 2 episodes
• Grounding — 18× in this paper · explained in 3 episodes
• World Model — 10× in this paper · explained in 7 episodes
• Benchmarking — 5× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• In International Conference on Machine Learning — 2× in this paper
• Optical Flow — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Verifiable Rewards — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• alpha — 2× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• Rethinking Video Generation Model for the Embodied World
• GEOPHYS: The Geometry of Physical Plausibility
• What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewards
• Learning Explicit Physical Parameter Control and Benchmarking for Video Generation
• RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
• Cosmos World Foundation Model Platform for Physical AI
• Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
• GPT-4o System Card
• Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation
• WorldGym: World Model as An Environment for Policy Evaluation
• T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
• Wan: Open and Advanced Large-Scale Video Generative Models
• MAGI-1: Autoregressive Video Generation at Scale
• Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• HunyuanVideo 1.5 Technical Report
• RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models