8 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
Multi-scale Graph Autoregressive Modeling: Molecular Property Prediction via Next Token Prediction
Zhuoyang Jiang, Yaosen Min, Peiran Jin +1
We present Connection-Aware Motif Sequencing (CamS), a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token pre…
Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning
Xuerui Su, Shufang Xie, Guoqing Liu +7
Recently, Large Language Models (LLMs) have rapidly evolved, approaching Artificial General Intelligence (AGI) while benefiting from large-scale reinforcement learning to enhance H…
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
Mingqian Ma, Guoqing Liu, Chuan Cao +12
Advances in natural language processing and large language models have sparked growing interest in modeling DNA, often referred to as the "language of life". However, DNA modeling…
E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products
Yunyang Li, Lin Huang, Zhihao Ding +10
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However…
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
Yuxuan Ren, Dihan Zheng, Chang Liu +7
In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning mod…