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PhysisForcing is a training framework that enhances embodied video generation for robotic manipulation by enforcing physical consistency through pixel-level trajectory alignment and semantic-level relational alignment losses in a DiT-based architecture, achieving notable improvements on benchmarks.
The paper proposes Astra, an agentic spatial reasoning framework that couples a reinforcement learning-trained VLM policy with a world simulator to generate novel-view observations for improved spatial reasoning in Vision-Language Models.