JEPA-Anything: Learning Predictive Models across Different Worlds
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet ...
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World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet ...
Video diffusion models repeatedly process long spatiotemporal token sequences during denoising, making attention a ma...
Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent yea...
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work...
On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a ...
GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocat...
Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they o...
Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motiv...
Document parsing converts document images into structured content and requires reliable performance across diverse la...
As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts su...
We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and ev...
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial...