5 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design
Charles Novak, Zhenqiao Song, Chuan Li +2
The ability to generate proteins with desired functions is a central goal of computational biology. Although natural language can express diverse design objectives, existing protei…
TurboPairFormer: Fast and Stable Protein Folding Model Training with an Optimized Triangle Attention Kernel
Yide Ran, Chelsea Lowman, Jan Domański +6
Triangular attention is a core computation in AlphaFold3-style biomolecular models, with cubic cost in token count. Its shared pair bias adds a gradient reduction across attention…
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…
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…
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…