Haotian Wang

Current video generation models face a critical trade-off between faithfully executing keyframes and producing natural-looking videos, with performance degrading under increased keyframe density.

DRIFT not only sets a new state-of-the-art in self-improvement for language models but also redefines how we can dynamically adapt learning strategies based on problem difficulty.

PLAN-S achieves a 42% reduction in collision rates while providing diverse driving style adaptations through a novel cost map approach.

Current audio-visual generation models struggle to maintain coherence and alignment when scaling to minute-long content, a problem exposed by the new LongAV-Compass benchmark.

Achieve 9.97% higher accuracy in cross-domain human activity recognition while simultaneously reducing computation by 6.4x with a new sensor data tokenization and attention mechanism.

Forget tedious human-in-the-loop DAgger – WM-DAgger uses World Models to synthesize corrective actions for robots, achieving impressive 93% success with just 5 demos.

Multi-resolution decomposition and diffusion models can boost time series forecasting accuracy by up to 10% over existing methods.

Forcing networks to perform well under varying sparsity constraints during training can surprisingly boost generalization, outperforming standard dense training.