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Realistic predictions in robotic manipulation now come with precise arm control, ensuring the right actions yield the expected outcomes.
Simulating 8.3 billion diverse personas reveals nuanced user interactions that traditional evaluations miss, transforming how we assess AI systems.
Current MLLMs struggle with video event prediction due to poor logical reasoning and visual information utilization, but a new "Chain of Events" training paradigm significantly boosts performance.
Finally, you can use human feedback and other real-world, non-differentiable rewards to fine-tune fast, few-step diffusion models.
