Fan Feng

Interaction-weighted Resampling transforms how robots learn to manipulate objects, yielding a 19.8% boost in performance and a breakthrough in real-world goal achievement.

Achieving high-fidelity cloud removal, IB-HFN outperforms traditional methods by effectively decoupling noise suppression from texture preservation.

Foundation model embeddings contain so much irrelevant detail that projecting them into a task-centric latent space actually *improves* world model quality and control performance.

Learn to detect unknown network attacks by explicitly modeling what they are *not*.