Denario项目：面向科学发现的深度知识人工智能体
The Denario project: Deep knowledge AI agents for scientific discovery
October 30, 2025
作者: 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
cs.AI
摘要
我们推出Denario——一款作为科研助手设计的AI多智能体系统。该系统能够执行多种任务，包括生成创意、文献检索、制定研究计划、编写与执行代码、绘制图表，以及起草和审阅科学论文。其模块化架构既可处理特定任务（如生成创意），也能通过Cmbagent深度研究后端实现端到端的科学分析。本文详细阐述了Denario及其模块架构，并通过展示其在天体物理学、生物学、生物物理学、生物医学信息学、化学、材料科学、数学物理、医学、神经科学和行星科学等多学科领域生成的AI论文来印证系统能力。Denario还擅长融合跨学科理念，我们特别展示了一篇将量子物理学与机器学习方法应用于天体物理数据的论文作为例证。我们报告了领域专家对这些论文的评估结果，包括量化评分和类同行评议反馈，进而剖析当前系统的优势、不足与局限。最后，我们探讨了AI驱动科研的伦理影响，并反思该技术与科学哲学的内在关联。代码已公开发布于https://github.com/AstroPilot-AI/Denario，用户也可通过https://huggingface.co/spaces/astropilot-ai/Denario 直接运行网页演示版，完整应用将部署至云端。
English
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.