6 papers
Spin-Weighted Spherical Harmonics Enable Complete and Scalable -Equivariant Networks
Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5
-equivariant networks are promising for 3D atomistic system modeling, yet their scalability is limited by the complexity of the Clebsch-Gordan Tensor Produc…
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…
Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
Yide Ran, Jianwen Xie, Minghui Wang +4
Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods suffer from scalability chall…
Training Language Models for Bilateral Trade with Private Information
Dirk Bergemann, Soheil Ghili, Xinyang Hu +2
Bilateral bargaining under incomplete information provides a controlled testbed for evaluating large language model (LLM) agent capabilities. Bilateral trade demands individual rat…
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…
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…