7 papers
BatteryMat: a hierarchical machine-learning and DFT framework for average-voltage screening of lithium-ion cathode materials
Jaehyung Lee, Charles Rhys Campbell, Kent Zhang +1
Density functional theory (DFT) predicts cathode voltages accurately but does not scale to the combinatorial chemical spaces of modern materials databases, while pure machine-learn…
AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org
Jaehyung Lee, Justin Ely, Kent Zhang +3
Agentic AI systems increasingly connect large language models (LLMs) to external scientific tools, yet whether and when tool access improves prediction accuracy remains uncharacter…
Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning
Pei Yang, Ke Zhang, Ji Wang +5
We present CRM (Multi-Agent Collaborative Reward Model), a framework that replaces a single black-box reward model with a coordinated team of specialist evaluators to improve robus…
OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency
Jun Wang, Yunxiang Yao, Wenwei Kuang +11
Large Language Models drive a wide range of modern AI applications but impose substantial challenges on large-scale serving systems due to intensive computation, strict latency con…
SEDM: Scalable Self-Evolving Distributed Memory for Agents
Haoran Xu, Jiacong Hu, Ke Zhang +6
Long-term multi-agent systems inevitably generate vast amounts of trajectories and historical interactions, which makes efficient memory management essential for both performance a…
Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
Ji Wang, Kashing Chen, Xinyuan Song +4
Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adap…