5 papers
Reliable LLM-Generated Programs for High-Energy Physics Experiments through Graph-Grounded Software Knowledge
Yue Sun, Tong Liu, Yipu Liao +2
Extracting physics information from modern particle-physics experiments requires multistage analyses implemented on top of large and highly interconnected software ecosystems. Gene…
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…
MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution
Chenghan Li, Mingchen Li, Yipu Liao +1
Research on long-term time series prediction has primarily relied on Transformer and MLP models, while the potential of convolutional networks in this domain remains underexplored.…
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…