Vai Viswanathan

For years Yann LeCun has argued that generative video models can't truly learn physics. DeepMind's Physics-IQ benchmark proved him right, reporting "a striking lack of physical understanding in current generative video models". The best one scored just 29.5%. This CVPR 2026 paper finds the fix in LeCun's own playbook. "Inference-time Physics Alignment" doesn't retrain the generator. It steers a video model's denoising at inference using a reward from VJEPA-2, LeCun's Joint Embedding Predictive Architecture. - Won first place in the ICCV 2025 PhysicsIQ Challenge with a 62.64% score, beating the previous state of the art by 7.42%. - Repurpose VJEPA-2's "surprise" score as a reward, then search and rank multiple candidate denoising trajectories at test time. - Why it matters: It's a neat vindication of LeCun's thesis. The pixel-prediction generator alone doesn't get physics, but the JEPA world model approach he champions supplies the physics the video model lacks.

44,077 views