The Denario project: Deep knowledge AI agents for scientific discovery
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.
6908169f812eca10f9cc5e12
Francisco Villaescusa-Navarro
6908169f812eca10f9cc5e13
Boris Bolliet
6908169f812eca10f9cc5e14
Pablo Villanueva-Domingo
claimed_verified
2025-11-06T11:48:53.677Z
6613dfa16fe9d892116b5f99
https://cdn-avatars.huggingface.co/v1/production/uploads/6613dfa16fe9d892116b5f99/wY46oty2NuGniO3ioynuK.png
Pablo Villanueva Domingo
PabloVD
user
PabloVD
6908169f812eca10f9cc5e15
Adrian E. Bayer
6908169f812eca10f9cc5e16
Aidan Acquah
6908169f812eca10f9cc5e17
Chetana Amancharla
6908169f812eca10f9cc5e18
Almog Barzilay-Siegal
6908169f812eca10f9cc5e19
Pablo Bermejo
6908169f812eca10f9cc5e1a
Camille Bilodeau
6908169f812eca10f9cc5e1b
Pablo Cárdenas Ramírez
6908169f812eca10f9cc5e1c
Miles Cranmer
6908169f812eca10f9cc5e1d
Urbano L. França
6908169f812eca10f9cc5e1e
ChangHoon Hahn
6908169f812eca10f9cc5e1f
Yan-Fei Jiang
6908169f812eca10f9cc5e20
Raul Jimenez
6908169f812eca10f9cc5e21
Jun-Young Lee
6908169f812eca10f9cc5e22
Antonio Lerario
6908169f812eca10f9cc5e23
Osman Mamun
6908169f812eca10f9cc5e24
Thomas Meier
6908169f812eca10f9cc5e25
Anupam A. Ojha
6908169f812eca10f9cc5e26
Pavlos Protopapas
6908169f812eca10f9cc5e27
Shimanto Roy
6908169f812eca10f9cc5e28
David N. Spergel
6908169f812eca10f9cc5e29
Pedro Tarancón-Álvarez
6908169f812eca10f9cc5e2a
Ujjwal Tiwari
6908169f812eca10f9cc5e2b
Matteo Viel
6908169f812eca10f9cc5e2c
Digvijay Wadekar
6908169f812eca10f9cc5e2d
Chi Wang
6908169f812eca10f9cc5e2e
Bonny Y. Wang
6908169f812eca10f9cc5e2f
Licong Xu
6908169f812eca10f9cc5e30
Yossi Yovel
6908169f812eca10f9cc5e31
Shuwen Yue
6908169f812eca10f9cc5e32
Wen-Han Zhou
6908169f812eca10f9cc5e33
Qiyao Zhu
6908169f812eca10f9cc5e34
Jiajun Zou
6908169f812eca10f9cc5e35
Íñigo Zubeldia
The Denario project: Deep knowledge AI agents for scientific discovery
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.
Denario, an AI multi-agent system, performs various scientific research tasks and generates papers across multiple disciplines, demonstrating its capabilities and limitations through expert evaluations.
deep-research backend Cmbagent AI-generated papers astrophysics biology biophysics biomedical informatics chemistry material science mathematical physics medicine neuroscience planetary science quantum physics machine learning