3 citations · 7 across the 15 of their papers we have counts for
15 papers
Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
Fang Wu, Weihao Xuan, Jure Leskovec +2
Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. However, existing methods rely on s…
ProCUA-SFT Technical Report
Jaehun Jung, Ximing Lu, Brandon Cui +11
Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data…
Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients
Byung-Kwan Lee, Ximing Lu, Shizhe Diao +8
Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitate logits from a much larger teac…
SurfDesign: Effective Protein Design on Molecular Surfaces
Fang Wu, Shuting Jin, Xiangru Tang +5
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We i…
NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
Fang Wu, Haokai Zhao, Da Xing +18
Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively throu…
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
Fang Wu, Weihao Xuan, Heli Qi +26
Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries…
RiboPO: Preference Optimization for Structure- and Stability-Aware RNA Design
Minghao Sun, Hanqun Cao, Zhou Zhang +10
Designing RNA sequences that reliably adopt specified three-dimensional structures while maintaining thermodynamic stability remains challenging for synthetic biology and therapeut…
L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks
Jiyu Cui, Fang Wu, Haokai Zhao +4
Large language models have demonstrated remarkable reasoning capabilities across diverse natural language tasks. However, comparable breakthroughs in scientific discovery are more…
From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
Hanqun Cao, Hongrui Zhang, Junde Xu +12
Protein language models (PLMs) have advanced computational protein science through large-scale pretraining and scalable architectures. In parallel, reinforcement learning (RL) has…
A deep reinforcement learning platform for antibiotic discovery
Hanqun Cao, Marcelo D. T. Torres, Jingjie Zhang +8
Antimicrobial resistance (AMR) is projected to cause up to 10 million deaths annually by 2050, underscoring the urgent need for new antibiotics. Here we present ApexAmphion, a deep…
Multiplayer Nash Preference Optimization
Fang Wu, Xu Huang, Weihao Xuan +8
Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models with human preferences. However, reward-based methods grou…
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
The Invisible Leash: Why RLVR May or May Not Escape Its Origin
Fang Wu, Weihao Xuan, Danny Xing +5
Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However, it remains unclear whether the curre…
When to Trust Context: Self-Reflective Debates for Context Reliability
Zeqi Zhou, Fang Wu, Shayan Talaei +5
Large language models frequently encounter conflicts between their parametric knowledge and contextual input, often resulting in factual inconsistencies or hallucinations. We propo…
PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking
Yize Jiang, Xinze Li, Yuanyuan Zhang +13
Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks…