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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.14288 |
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| _version_ | 1866915742358700032 |
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| author | Peng, Ze-Yu Yuan, Hao-Shi Lai, Qi Jiang, Jun-Qian Ye, Gen Zhang, Jun Piao, Yun-Song |
| author_facet | Peng, Ze-Yu Yuan, Hao-Shi Lai, Qi Jiang, Jun-Qian Ye, Gen Zhang, Jun Piao, Yun-Song |
| contents | We present \textbf{DeepInflation}, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi-agent architecture, \textbf{DeepInflation} integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval-augmented generation (RAG) knowledge base. This framework enables the agent to automatically explore and verify the vast landscape of inflationary potentials while grounding its outputs in established theoretical literature. We demonstrate that \textbf{DeepInflation} can successfully discover simple and viable single-field slow-roll inflationary potentials consistent with the latest observations (here ACT DR6 results as example) or any given $n_s$ and $r$, and provide accurate theoretical context for obscure inflationary scenarios. \textbf{DeepInflation} serves as a prototype for a new generation of autonomous scientific discovery engines in cosmology, which enables researchers and non-experts alike to explore the inflationary landscape using natural language. This agent is available at https://github.com/pengzy-cosmo/DeepInflation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14288 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepInflation: an AI agent for research and model discovery of inflation Peng, Ze-Yu Yuan, Hao-Shi Lai, Qi Jiang, Jun-Qian Ye, Gen Zhang, Jun Piao, Yun-Song Cosmology and Nongalactic Astrophysics Artificial Intelligence Computational Engineering, Finance, and Science General Relativity and Quantum Cosmology High Energy Physics - Theory We present \textbf{DeepInflation}, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi-agent architecture, \textbf{DeepInflation} integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval-augmented generation (RAG) knowledge base. This framework enables the agent to automatically explore and verify the vast landscape of inflationary potentials while grounding its outputs in established theoretical literature. We demonstrate that \textbf{DeepInflation} can successfully discover simple and viable single-field slow-roll inflationary potentials consistent with the latest observations (here ACT DR6 results as example) or any given $n_s$ and $r$, and provide accurate theoretical context for obscure inflationary scenarios. \textbf{DeepInflation} serves as a prototype for a new generation of autonomous scientific discovery engines in cosmology, which enables researchers and non-experts alike to explore the inflationary landscape using natural language. This agent is available at https://github.com/pengzy-cosmo/DeepInflation. |
| title | DeepInflation: an AI agent for research and model discovery of inflation |
| topic | Cosmology and Nongalactic Astrophysics Artificial Intelligence Computational Engineering, Finance, and Science General Relativity and Quantum Cosmology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2601.14288 |