arXiv:2601.14288v2 Announce Type: replace-cross Abstract: 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) 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 DeepInflation can successfully discover simple and viable single-field slow-roll inflationary potentials consistent with the latest observations (with the ACT DR6 results taken as an example) or any given $n_s$ and $r$, and provide accurate theoretical context for obscure inflationary scenarios. 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.

arXiv:2601.18652v4 Announce Type: replace-cross Abstract: Galaxy clusters are the largest virialized structures in the Universe and are predominantly dominated by dark matter. The hydrostatic mass and the mass obtained from gravitational lensing measurements generally differ, a discrepancy known as the hydrostatic mass bias. In this work, we derive the hydrostatic mass of galaxy clusters within the framework of Rastall gravity. We consider two scenarios: (i) the absence of dark matter and (ii) the presence of dark matter. In both cases, we constrain the Rastall parameter in the cluster-scale using observational data. In the first scenario, Rastall gravity effectively reduces the hydrostatic mass, bringing it closer to the observed baryonic mass. The best linear fit yields a slope $\mathbf{M}=1.07\pm0.11$, indicating a near one-to-one correspondence between the two masses. In the second scenario, Rastall gravity helps to alleviate the hydrostatic mass bias. The linear fit between the Rastall hydrostatic mass and the observed lensing mass results in a best-fit slope $\mathbf{M}=0.99\pm0.26$, which is very close to unity. We also calculate the goodness-of-fit for every fit. The statistical evaluations indicate that Rastall gravity provides a viable phenomenological framework that can improve certain aspects of the mass discrepancy problem at the level of scaling relations. However, it does not universally outperform other modified gravity model, when evaluated using standard goodness-of-fit criteria.