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cosmosage

Welcome to cosmosage—an advanced AI assistant designed for those curious about the universe we live in. cosmosage was trained on thousands of papers and books and will try its best to answer your questions about cosmology. However, while cosmosage is a powerful tool for brainstorming, expanding your knowledge, and exploring new ideas, keep in mind that it's still an LLM and may sometimes produce inaccurate responses.

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AstroSage-70B is currently available as a demo. Computational resources are limited, so generations may be slow, and this service will only be available for a limited time.

The model weights are also freely available at https://huggingface.co/AstroMLab/AstroSage-70B If you have access to a sufficient GPU resource, try running your own inference.

Please note: In November 2025, the database was reset. All users will need to create a new account by clicking "Sign Up".

Talk to cosmosage

Get paper recommendations

Stay up to date with the latest developments in cosmology and instrumentation. The machine learning-powered recommendation service was adapted from Andrej Karpathy's arxiv-sanity-lite and allows you to tag papers and receive relevant suggestions. In my experience, after tagging about five papers you like, the recommendations will become relevant to your interests.

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About cosmosage

Learn more about the development and capabilities of cosmosage by reading the following papers.

cosmosage: A natural-language assistant for cosmology

Tijmen de Haan

Published in Astronomy and Computing, Volume 51 (2025).

https://doi.org/10.1016/j.ascom.2025.100934


Achieving GPT-4o level performance in astronomy with a specialized 8B-parameter large language model

Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Azton Wells, Nesar Ramachandra, Rui Pan, Zechang Sun

Published in Scientific Reports, Volume 15 (2025).

https://doi.org/10.1038/s41598-025-97131-y


AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model

Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Emily Herron, Vanessa Lama, Rui Pan, Azton Wells, and Nesar Ramachandra

arXiv:2505.17592 (2025), accepted contribution to Astro4ML (ICML 2025).

https://arxiv.org/abs/2505.17592