Speaker
Description
We present a prototype Retrieval-Augmented Generation (RAG) and agentic LLM tool designed to accelerate and support high-energy physics analyses. As a case study, we applied the system to the published 2016 Λb → Λγ Run 2 analysis. Reproducing legacy workflows is often slow and error-prone due to fragmented code, dispersed documentation, personnel turnover, and software evolution over multiple data-taking periods. Our tool integrates semantic retrieval, code-understanding capabilities, and autonomous agent actions to navigate these challenges.
The system ingests heterogeneous knowledge sources—including archived analysis scripts, notes, published documentation, and the modern LHCb software stack—and uses a vector database to build a unified retrieval layer. Built on general-purpose LLMs (e.g., Claude, GPT-x), the assistant can propose analysis strategies, generate or repair scripts, and autonomously perform several reconstruction and analysis tasks. In internal tests, the prototype demonstrated the potential to compress workflows that normally require months or years of manual effort.
We discuss the tool’s architecture, agentic capabilities, and safeguards, as well as its current limitations. Verification is ongoing to evaluate reliability, detect errors, and benchmark performance against non-RAG LLM baselines. This early case study highlights the promise of RAG-augmented, agent-driven systems in restoring, verifying, and accelerating complex HEP analyses, pointing toward future end-to-end AI-assisted analysis pipelines.