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.

翻译：本文提出\textbf{DeepInflation}，一个专为暴胀宇宙学研究和模型发现而设计的AI智能体。该智能体基于多智能体架构构建，将大语言模型（LLMs）与符号回归（SR）引擎以及检索增强生成（RAG）知识库相结合。该框架使智能体能够自动探索并验证广阔的暴胀势能景观，同时将其输出建立在已确立的理论文献基础上。我们证明，\textbf{DeepInflation}能够成功发现与最新观测结果（此处以ACT DR6结果为例）或任何给定的$n_s$和$r$一致的、简单且可行的单场慢滚暴胀势能，并为晦涩的暴胀场景提供准确的理论背景。\textbf{DeepInflation}是新一代宇宙学自主科学发现引擎的原型，使研究人员和非专家都能使用自然语言探索暴胀景观。该智能体可在https://github.com/pengzy-cosmo/DeepInflation获取。