collaborators

5 papers

cs.LG2026

Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning

Ziwen Liu, Huawei Lin, Yide Ran +5

Large language models (LLMs) sometimes memorize undesirable knowledge, which must be removed after deployment. Prior work on machine unlearning has focused largely on optimization…

cs.CL2026

ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents

Zhuofeng Li, Yi Lu, Dongfu Jiang +7

The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-based reviewers often generate s…

cs.CV2026

PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction

Xiang Zhang, Sohyun Yoo, Hongrui Wu +3

We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed…

cs.LG2025

To Compress or Not? Pushing the Frontier of Lossless GenAI Model Weights Compression with Exponent Concentration

Zeyu Yang, Tianyi Zhang, Jianwen Xie +3

The scaling of Generative AI (GenAI) models into the hundreds of billions of parameters makes low-precision computation indispensable for efficient deployment. We argue that the fu…

cs.LG2025

InstructPro: Natural Language Guided Ligand-Binding Protein Design

Zhenqiao Song, Ramith Hettiarachchi, Chuan Li +2

The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by sca…