The Denario project: Deep knowledge AI agents for scientific discovery
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Villaescusa-Navarro, Francisco Bolliet, Boris Villanueva-Domingo, Pablo Bayer, Adrian E. Acquah, Aidan Amancharla, Chetana Barzilay-Siegal, Almog Bermejo, Pablo Bilodeau, Camille Ramírez, Pablo Cárdenas Cranmer, Miles França, Urbano L. Hahn, ChangHoon Jiang, Yan-Fei Jimenez, Raul Lee, Jun-Young Lerario, Antonio Mamun, Osman Meier, Thomas Ojha, Anupam A. Protopapas, Pavlos Roy, Shimanto Spergel, David N. Tarancón-Álvarez, Pedro Tiwari, Ujjwal Viel, Matteo Wadekar, Digvijay Wang, Chi Wang, Bonny Y. Xu, Licong Yovel, Yossi Yue, Shuwen Zhou, Wen-Han Zhu, Qiyao Zou, Jiajun Zubeldia, Íñigo |
| author_facet | Villaescusa-Navarro, Francisco Bolliet, Boris Villanueva-Domingo, Pablo Bayer, Adrian E. Acquah, Aidan Amancharla, Chetana Barzilay-Siegal, Almog Bermejo, Pablo Bilodeau, Camille Ramírez, Pablo Cárdenas Cranmer, Miles França, Urbano L. Hahn, ChangHoon Jiang, Yan-Fei Jimenez, Raul Lee, Jun-Young Lerario, Antonio Mamun, Osman Meier, Thomas Ojha, Anupam A. Protopapas, Pavlos Roy, Shimanto Spergel, David N. Tarancón-Álvarez, Pedro Tiwari, Ujjwal Viel, Matteo Wadekar, Digvijay Wang, Chi Wang, Bonny Y. Xu, Licong Yovel, Yossi Yue, Shuwen Zhou, Wen-Han Zhu, Qiyao Zou, Jiajun Zubeldia, Íñigo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Denario project: Deep knowledge AI agents for scientific discovery Villaescusa-Navarro, Francisco Bolliet, Boris Villanueva-Domingo, Pablo Bayer, Adrian E. Acquah, Aidan Amancharla, Chetana Barzilay-Siegal, Almog Bermejo, Pablo Bilodeau, Camille Ramírez, Pablo Cárdenas Cranmer, Miles França, Urbano L. Hahn, ChangHoon Jiang, Yan-Fei Jimenez, Raul Lee, Jun-Young Lerario, Antonio Mamun, Osman Meier, Thomas Ojha, Anupam A. Protopapas, Pavlos Roy, Shimanto Spergel, David N. Tarancón-Álvarez, Pedro Tiwari, Ujjwal Viel, Matteo Wadekar, Digvijay Wang, Chi Wang, Bonny Y. Xu, Licong Yovel, Yossi Yue, Shuwen Zhou, Wen-Han Zhu, Qiyao Zou, Jiajun Zubeldia, Íñigo Artificial Intelligence Computation and Language Machine Learning Multiagent Systems 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. |
| title | The Denario project: Deep knowledge AI agents for scientific discovery |
| topic | Artificial Intelligence Computation and Language Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2510.26887 |