Robotic manipulation achieves high success rates by integrating goal-conditioned world models with multi-scale temporal control, unlike traditional methods.

Real-time object detection achieves state-of-the-art accuracy and speed by using dynamic expert networks instead of static computation, improving efficiency and performance.

Video super-resolution achieves faster processing by using auto-regressive diffusion instead of slow sequential processing, improving online video quality.

Visual question answering achieves state-of-the-art accuracy by using bidirectional perceptual shaping instead of relying on inference-time visual cues, improving performance.

Science education transforms into engaging hands-on experiments covering elasticity, capillarity, oscillations, flight, friction, perception and thermal expansion instead of passive lectures, making complex physics concepts tangible through interactive activities.

Image generation achieves high quality by learning reverse models instead of explicit inverses, improving speed and flexibility.

Real-time video generation improves by using improved on-policy distillation instead of slow sequential processing, enabling seamless human-AI interaction.

Surgical robots learn faster by using videos and world modeling instead of relying on limited datasets, improving precision and reducing fatigue.

Autonomous agents achieve better task completion by proactively seeking evidence instead of relying on passive verification, improving efficiency and reliability.