Yuchen Yan

Explicit 3D geometry, rendered as text, unlocks superior spatial reasoning in RGB-only VLMs by guiding trajectory correction during distillation.

Computer-use agents can bypass brittle, hand-engineered APIs entirely by learning when to substitute mouse clicks with native shell commands, yielding a 14.8-point leap on OSWorld.

PILL achieves up to 6.0 BLEU-2 improvement on text infilling while running 1.82x faster than the best existing method, revolutionizing efficiency in diffusion language models.

PaperGym achieves a remarkable 73.48 on ResearchQA, outperforming larger models and redefining how AI can generate and evaluate research plans.

Bridging the gap between inference and adaptation in VLMs could lead to significant performance boosts by ensuring robust pseudo-labels that accurately reflect sample-level relationships.

Scaling zero RL to a trillion parameters reveals that models can spontaneously develop advanced cognitive behaviors, making traditional heuristics obsolete.

Harness VLA boosts the performance of frozen VLA models by 38.6 percentage points on challenging manipulation tasks without the need for finetuning.

Even frontier models like Claude Sonnet 4.6 stumble when asked to infer user preferences and proactively assist in mobile tasks, achieving less than 50% success despite excelling at explicit task execution.

LLMs can escape the trap of confidently wrong reasoning by co-evolving a generator and verifier from a single model, bootstrapping each other to break free from flawed consensus.