MadAgents

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Authors: Tilman Plehn, Daniel Schiller, Nikita Schmal
Cited by: 0

Abstract

We uncover an effective and communicative set of agents working with MadGraph. Agentic installation, learning-by-doing training, and user support provide easy access to state-of-the-art simulations and accelerate LHC research. We show in detail how MadAgents interact with inexperienced and advanced users, support a range of simulation tasks, and analyze the results. In a second step, we illustrate how MadAgents automatize event generation and run an autonomous simulation campaign, starting from a pdf file of a paper. We also present successive MadAgents updates, including a Claude Code implementation with a self-improvement loop.

Fields of Study & Subjects

Particle physics theoretical and experimental studies Scientific Computing and Data Management Artificial Intelligence Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
28 January 2026
Document type
Preprint