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arXiv · 2601.21015

MadAgents

Tilman Plehn·Daniel Schiller·Nikita Schmal
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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 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. The updated Claude Code implementation includes a self-improvement loop.

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Tilman Plehn, Daniel Schiller, Nikita Schmal. 2026-01-28. MadAgents. https://arxiv.org/abs/2601.21015

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