Yi-Chao Yan

HarnessEval-W: Agentifying the Evaluation of Visual Worlds

A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Humans spot such violations natu...

RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models

Generative models learn visual structure through denoising, yet their internal states are entangled with both noise level and network depth, making it difficult to obtain a stable visual representation from the generator itself. We introduce RepFlow, which learns such a representation from the generator's evolving comp...

Enfold: Folding World-Generator Computation into Predictive Representations for Efficient Embodied Control

Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control

This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.

Spatiotemporally Decoupled Autoregressive Diffusion Model for Human Motion Generation

A unified spatiotemporally decoupled framework named DeMoDiff is proposed, which jointly redesigns representation and architecture and incorporates spatial-temporal masking and attention mechanisms into an autoregressive diffusion generator, achieving both generative capability and controllable editability.