Speaker
Description
Agentic AI frameworks offer a promising path toward automated HEP analyses, but current approaches rely on iterative prompting and accumulated context, leading to limitations in reproducibility and generality. We try to address these limitations by designing a structured agentic framework that drives a physics foundation model (FM) as its engine, replacing ad-hoc prompt engineering with systematic workflow steps.
We demonstrate the framework using EveNet as a case study, on two benchmarks: a search for exotic Higgs boson decays (H→aa→4b) and a measurement of quantum correlations in dileptonic tt̄ production. Given a natural-language physics goal, the agent selects the appropriate task-specific components of the FM, confirms with the physicist, converts input data, monitors fine-tuning, and computes the target observable. We will present the results of both benchmarks and discuss the remaining challenges toward fully reproducible agentic HEP analyses