The FERMIACC: Agents for Particle Theory
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912979978551296 |
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| author | Agrawal, Prateek Craig, Nathaniel Madden, Amalia Lombera, Iñigo Valenzuela |
| author_facet | Agrawal, Prateek Craig, Nathaniel Madden, Amalia Lombera, Iñigo Valenzuela |
| contents | We present the FERMIACC, a scaffolded reasoning model built on OpenAI agents designed to autonomously generate and quantitatively validate theory hypotheses for high energy physics data at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22538 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The FERMIACC: Agents for Particle Theory Agrawal, Prateek Craig, Nathaniel Madden, Amalia Lombera, Iñigo Valenzuela High Energy Physics - Phenomenology We present the FERMIACC, a scaffolded reasoning model built on OpenAI agents designed to autonomously generate and quantitatively validate theory hypotheses for high energy physics data at scale. |
| title | The FERMIACC: Agents for Particle Theory |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2603.22538 |