5 papers
\textbf{\textsc{HepUKE}}: Uncertainty-aware Knowledge Evolution for High Energy Physics Analysis Agents
Zijie Shang, Ke Li, Junkun Jiao +6
Knowledge bases are essential to agents for high energy physics (HEP), supplying few-shot evidence for previously unseen tasks. Their principal utility lies in strategic guidance f…
HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics
Junkun Jiao, Tong Liu, Ke Li +6
The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automatio…
Dr.Sai: An agentic AI for real-world physics analysis at BESIII
Mingfeng He, Fayu Jiang, Junkun Jiao +16
High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analys…
AI Agents, Language, Deep Learning and the Next Revolution in Science
Ke Li, Beijiang Liu, Bruce Mellado +2
Modern science is reaching a critical inflection point. Instruments across disciplines, from particle physics and astronomy to genomics and climate modeling, now produce data of su…
Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
Zhengde Zhang, Yiyu Zhang, Haodong Yao +10
Large Language Models (LLMs) are undergoing a period of rapid updates and changes, with state-of-the-art (SOTA) model frequently being replaced. When applying LLMs to a specific sc…