MadAgents
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866914453163868160 |
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| author | Plehn, Tilman Schiller, Daniel Schmal, Nikita |
| author_facet | Plehn, Tilman Schiller, Daniel Schmal, Nikita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MadAgents Plehn, Tilman Schiller, Daniel Schmal, Nikita High Energy Physics - Phenomenology 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. |
| title | MadAgents |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2601.21015 |