Yilin Zhang

The paper proposes DUDE, a two-stage framework that significantly reduces the susceptibility of web agents to deceptive user interfaces by integrating deception detection into the agent's learning process.

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion.

This paper introduces X-Stage, a software-visible post-issue pipeline stage to improve communication efficiency in distributed diffusion transformer (DiT) inference, leading to significant speedups for DeepGEMM MegaMoE and Ulysses sequence-parallel attention.