A new approach to physical world models — reason about "how the world changes" in language before generating any pixels. Title: PhiZero: A World Model Built Around Physical Language ❓ What is "Physical Language"? 💡 A discrete token sequence that directly represents state-transition patterns in a video — separate from visual appearance. Because dynamics and appearance are disentangled, the state transitions encoded from one video can be transferred to a completely different visual style. PhiZero's "Reason-then-render" paradigm predicts this physical language first, then generates video from it. ❓ Why did previous physical world models produce physically inconsistent results? 💡 They relied on direct pixel-space prediction, burying dynamics implicitly inside the model. Reconciling visual realism with physical consistency was difficult — leading to failures like a tennis ball hitting a rubber duck that doesn't react. The reasoning process was also opaque by design. ❓ What performance numbers did it achieve? 💡 State-of-the-art results across all six benchmarks: ・Physics-IQ IQ-Score: 41.2 (beats previous SOTA of 39.5) ・WorldModelBench: highest in physical compliance, commonsense, and overall score ・YoCausal causal reasoning: aggregated rank 2.0 (best) ・Tokenizer: just 256 tokens achieve PSNR 28.9 — 1/175th of Wan2.2-5B VAE's token count ❓ What does this mean for robotics and autonomous driving? 💡 Physical Language is appearance-agnostic, so zero-shot sim-to-real and human-to-robot transfer become possible. The team demonstrated encoding LIBERO simulation dynamics and replaying them as realistic video, and directly transferring human motion patterns to robot embodiments — all without task-specific training. #WorldModel# #EmbodiedAI#