Wenqiao Zhang

Treating robot skill calls as runtime-verified execution proposals rather than blind action predictions enables self-correcting VLA autonomy that reaches 97.4% success on LIBERO.

Ditch slow, irrelevant text-based reasoning: VISUALTHINK-VLA uses visual tokens to speed up vision-language-action policies by 22x while boosting accuracy.

LLMs stumble in multi-turn conversations not just because of context length, but because they poison themselves with their own past mistakes – and you can fix it with self-distillation.

InstructSAM equips SAM with high-level instruction understanding and compositional reasoning for multi-instance segmentation, all without modifying SAM's core architecture.

Forget retraining: this anomaly detection framework adapts to evolving data streams on-the-fly using a hypernetwork to shift parameters, achieving state-of-the-art performance.

Achieve industrial anomaly detection that not only locates defects, but explains them and generates controlled edits, all in one model.

Object-centric vision could be the key to unlocking LMMs' potential for precise object manipulation and fine-grained spatial reasoning, capabilities currently beyond their reach.

An 8B model can now generate scientific graphics code that rivals or surpasses the output of much larger proprietary models, thanks to a new dataset, benchmark, and reinforcement learning approach.

A new unified LVLM, OmniCT, bridges the gap between slice-level detail and volumetric understanding in CT scans, outperforming existing methods by a significant margin.