Chuanhao Li

Marionette achieves robust long-horizon control in interactive game worlds by explicitly modeling the evolving state while maintaining photorealistic appearance without fidelity loss.

Evoke achieves state-of-the-art performance in interactive world modeling by externalizing memory and enabling long-horizon supervision, all while maintaining responsiveness and efficiency.

Conditioning signals now match generated content more closely, leading to unprecedented realism in long-horizon video generation.

Simulation traces can transform LLMs into powerful tools for diagnosing and improving complex scheduling policies, achieving unprecedented performance gains.

Self-evolving LLM agents can now break through their capability limits by learning from challenging examples without needing trajectory annotations.

AlayaWorld achieves unprecedented long-horizon video generation with just four sampling steps, revolutionizing how we create interactive virtual environments.

Surprise Forcing redefines resource allocation in video generation, leading to improved visual quality and consistency without sacrificing streaming speed.

Interactive game worlds can now leverage a scalable data engine that captures 90 hours of gameplay, setting a new standard for state-aware modeling.

AlayaWorld transforms game development by enabling real-time, interactive world generation that adapts to user actions without the need for extensive manual design.

LLM agents can dramatically improve their long-horizon decision-making by strategically managing memory, with performance varying significantly based on memory structure.

Runtime pass rates plummet from 80.4% to 5.7% as project complexity increases, exposing critical architectural flaws in code generation models.

Forget training on long videos – PackForcing achieves state-of-the-art long-video generation by cleverly compressing the KV-cache into Sink, Mid, and Recent tokens, enabling 24x temporal extrapolation from short-video training.