Codebase release 2.0 for MadAgents
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.
Authors
- Tilman Plehn (ORCID: https://orcid.org/0000-0001-5660-7790)
- Daniel Schiller (ORCID: https://orcid.org/0009-0009-6064-4565)
- Nikita Schmal (ORCID: https://orcid.org/0009-0003-2306-7347)
Institutions
- Heidelberg University (DE)
Publication Details
- Journal
- SciPost Physics Codebases
- Published
- 2026-11-05
- DOI
- https://doi.org/10.21468/scipostphyscodeb.86-r2.0
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00