DeepInflation: an AI agent for research and model discovery of inflation
- lab arXiv
- lab arXivLabs
- location astro-ph.CO
- model DeepInflation
- person Ze-Yu Peng
- product CatalyzeX Code Finder for Papers
- product DagsHub
- product alphaXiv
Researchers have presented DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology, integrating Large Language Models with a symbolic regression engine and a retrieval-augmented generation knowledge base[1].
DeepInflation can automatically explore and verify the vast landscape of inflationary potentials, discovering simple and viable single-field slow-roll inflationary potentials consistent with the latest observations, such as the ACT DR6 results. It also provides accurate theoretical context for obscure inflationary scenarios[1]. Meanwhile, a critique of agent models in AI has been presented, arguing that genuine agency requires internalized structures within the system, rather than external scaffolding. This critique references Descartes' grounding of agency in independent thought and proposes the Goal-Identity-Configurator (GIC) architecture as a general-purpose agent model[2]. The rise of Large Language Model (LLM) systems has led to concerns about AI escaping human control with destructive power, with agent models being marketed as 'coding agents' and 'AI co-scientists' that promise to drive up productivity[2].
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Background sources we checked (10)
- arxiv.org ↗ We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi-agent architecture, DeepInflation integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval-augmented generation (RAG…
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