Technical Report
Technical reports detail the development and evaluation of novel methods and systems across diverse scientific domains. Current research focuses on improving the performance and efficiency of large language models (LLMs), including advancements in model architectures like transformers and the use of techniques such as retrieval-augmented generation and knowledge distillation. These improvements are driving progress in areas such as autonomous programming, biomolecular structure prediction, and AI-powered solutions for industrial applications, ultimately accelerating scientific discovery and technological innovation.
Papers
Improving the State of the Art for Training Human-AI Teams: Technical Report #3 -- Analysis of Testbed Alternatives
Lillian Asiala, James E. McCarthy, Lixiao Huang
Improving the State of the Art for Training Human-AI Teams: Technical Report #2 -- Results of Researcher Knowledge Elicitation Survey
James E. McCarthy, Lillian Asiala, LeeAnn Maryeski, Dawn Sillars
Improving the State of the Art for Training Human-AI Teams: Technical Report #1 -- Results of Subject-Matter Expert Knowledge Elicitation Survey
James E. McCarthy, Lillian Asiala, LeeAnn Maryeski, Nyla Warren