SMEFT-Pheno-Agent: a natural-language-driven AI agent for machine-learning-assisted Standard Model Effective Field Theory phenomenology

Abstract

We present SMEFT-Pheno-Agent, a Python workflow guided by a natural-language AI agent to perform machine-learning-assisted Standard Model Effective Field Theory (SMEFT) phenomenology at high-energy colliders. The software coordinates twelve automated execution phases spanning configuration intake, environment validation, event generation, machine-learning selection, statistical inference, and final audit. At each phase boundary, the agent interprets natural-language intent to generate runnable parameter files and adapter invocations required for subsequent execution. Once the detector-level events are written, the agent automatically proposes key kinematic observables alongside candidate machine-learning algorithms suited to the specific data structure and analysis objectives. All numerical calculations are delegated strictly to validated domain tools, with MadGraph5_aMC@NLO, Pythia, Delphes generating collider simulations, and MLAnalysis extracting features. The agent cannot modify physical parameters outside the locked configuration, and all LLM-produced artifacts, including parameter files, observable choices, algorithm selections, and prose drafts, are documented in machine-readable phase manifests prior to execution. These manifests establish complete reproducibility and audit traceability for SMEFT phenomenology studies.