6 papers
Protein Structure Tokenization via Geometric Byte Pair Encoding
Michael Sun, Weize Yuan, Gang Liu +2
Protein structure is central to biological function, and enabling multimodal protein models requires joint reasoning over sequence, structure, and function. A key barrier is the la…
Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation
Xiang Li, Gang Liu, Weitao Zhou +2
Imitation learning (IL) has emerged as a central paradigm in autonomous driving. While IL excels in matching expert behavior in open-loop settings by minimizing per-step prediction…
Graph Diffusion Transformers are In-Context Molecular Designers
Gang Liu, Jie Chen, Yihan Zhu +4
In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL con…
Advancing Speech Summarization in Multi-modal LLMs with Reinforcement Learning
Shaoshi Ling, Gang Liu, Guoli Ye +1
Speech summarization is a critical component of spoken content understanding, particularly in the era of rapidly growing spoken and audiovisual data. Recent advances in multi-modal…
Directed Graph Grammars for Sequence-based Learning
Michael Sun, Orion Foo, Gang Liu +2
Directed acyclic graphs (DAGs) are a class of graphs commonly used in practice, with examples that include electronic circuits, Bayesian networks, and neural architectures. While m…
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages
Michael Sun, Weize Yuan, Gang Liu +2
Recent data-efficient molecular generation approaches exploit graph grammars to introduce interpretability into the generative models. However, grammar learning therein relies on e…