The Denario project: Deep knowledge AI agents for scientific discovery

Francisco Villaescusa-Navarro; Boris Bolliet; Pablo Villanueva-Domingo; Adrian E. Bayer; Aidan Acquah; Chetana Amancharla; Almog Barzilay-Siegal; Pablo Bermejo; Camille Bilodeau; Pablo Cárdenas Ramírez; Miles Cranmer; Urbano L. França; ChangHoon Hahn; Yan-Fei Jiang; Raul Jimenez; Jun-Young Lee; Antonio Lerario; Osman Mamun; Thomas Meier; Anupam A. Ojha; Pavlos Protopapas; Shimanto Roy; David N. Spergel; Pedro Tarancón-Álvarez; Ujjwal Tiwari; Matteo Viel; Digvijay Wadekar; Chi Wang; Bonny Y. Wang; Licong Xu; Yossi Yovel; Shuwen Yue; Wen-Han Zhou; Qiyao Zhu; Jiajun Zou; Íñigo Zubeldia

arXiv · 2510.26887

The Denario project: Deep knowledge AI agents for scientific discovery

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Abstract

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

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Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer, Aidan Acquah, Chetana Amancharla, Almog Barzilay-Siegal, Pablo Bermejo, Camille Bilodeau, Pablo Cárdenas Ramírez, Miles Cranmer, Urbano L. França, ChangHoon Hahn, Yan-Fei Jiang, Raul Jimenez, Jun-Young Lee, Antonio Lerario, Osman Mamun, Thomas Meier, Anupam A. Ojha, Pavlos Protopapas, Shimanto Roy, David N. Spergel, Pedro Tarancón-Álvarez, Ujjwal Tiwari, Matteo Viel, Digvijay Wadekar, Chi Wang, Bonny Y. Wang, Licong Xu, Yossi Yovel, Shuwen Yue, Wen-Han Zhou, Qiyao Zhu, Jiajun Zou, Íñigo Zubeldia. 2025-10-30. The Denario project: Deep knowledge AI agents for scientific discovery. https://arxiv.org/abs/2510.26887

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