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cs.LG2026
APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
Shentong Mo, Yatao Bian
Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While flow-matching models (, FlowDPO) have recently shown promise i…
cs.LG2026
Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions
Yongquan Shi, Zijing Ou, Shiping Wang +1
Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and product recommendation. Recent work…