Publications (6)
AgentOmnia: Scaling Agentic Models for Full-Scenario Applications
Hao Jiang, Gangtao Xin, Yingdi Huang +35
Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-sc…
Q-Drug: a Framework to bring Drug Design into Quantum Space using Deep Learning
Zhaoping Xiong, Xiaopeng Cui, Xinyuan Lin +5
Optimizing the properties of molecules (materials or drugs) for stronger toughness, lower toxicity, or better bioavailability has been a long-standing challenge. In this context, w…
Enhancing Memory Efficiency in Large Language Model Training Through Chronos-aware Pipeline Parallelism
Xinyuan Lin, Chenlu Li, Zongle Huang +5
Larger model sizes and longer sequence lengths have empowered the Large Language Model (LLM) to achieve outstanding performance across various domains. However, this progress bring…
Application-Driven Architecture Exploration for Cross-Layer Heterogeneous Systems
Yuchen Fan, Minghong Sun, Jikui Ma +19
AI and HPC infrastructure increasingly serves workload portfolios that combine dense tensor computation, sparse kernels, large memory footprints, and communication-intensive collec…
Multi-scale Optimal Transport for Complete Collider Events
Tianji Cai, Nathaniel Craig, Katy Craig +1
Building upon the success of optimal transport metrics defined for single collinear jets, we develop a multi-scale framework that models entire collider events as distributions on…
Hecaton: Training Large Language Models with Scalable Chiplet Systems
Zongle Huang, Shupei Fan, Chen Tang +3
Large Language Models (LLMs) have achieved remarkable success in various fields, but their training and finetuning require massive computation and memory, necessitating parallelism…