# The Denario project: Deep knowledge AI agents for scientific discovery

arXiv: https://arxiv.org/abs/2510.26887 · HTML: https://mlsanity.com/paper/2510.26887/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

## Built on by (most cited first, 21 of 21)

- [CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution](https://mlsanity.com/paper/2512.23880/) (2025)
- [Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper](https://mlsanity.com/paper/2511.04583/) (2025)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Towards autonomous quantum physics research using LLM agents with access to intelligent tools](https://mlsanity.com/paper/2511.11752/) (2025)
- [MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models](https://mlsanity.com/paper/2602.15951/) (2026)
- [Unlocking LLM Creativity in Science through Analogical Reasoning](https://mlsanity.com/paper/2605.11258/) (2026)
- [gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy](https://mlsanity.com/paper/2605.11269/) (2026)
- [Enhancing Agentic Autonomous Scientific Discovery with Vision-Language Model Capabilities](https://mlsanity.com/paper/2511.14631/) (2025)
- [Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research](https://mlsanity.com/paper/2604.09621/) (2026)
- [Plausible but Wrong: A case study on Agentic Failures in Astrophysical Workflows](https://mlsanity.com/paper/2604.25345/) (2026)
- [Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves](https://mlsanity.com/paper/2605.11280/) (2026)
- [Physics Is All You Need? A Case Study in Physicist-Supervised AI Development of Scientific Software](https://mlsanity.com/paper/2605.30353/) (2026)
- [AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions](https://mlsanity.com/paper/2606.22859/) (2026)
- [OmniScientist: An Omni-Modal Omni-Discipline AI Scientist](https://mlsanity.com/paper/2608.13558/) (2026)
- [Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers](https://mlsanity.com/paper/2604.01128/) (2026)
- [Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods](https://mlsanity.com/paper/2606.26130/) (2026)
- [Physics-Informed Neural Embeddings of PDE Solution Families](https://mlsanity.com/paper/2607.06348/) (2026)
- [Plato-Bio: verification-first biological novelty screening with temporal rediscovery and structural benchmarks](https://mlsanity.com/paper/2607.23975/) (2026)
- [AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review](https://mlsanity.com/paper/2607.25672/) (2026)
- [AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology II: Project Planning and Proposal Evaluation](https://mlsanity.com/paper/2607.25881/) (2026)
- [Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems](https://mlsanity.com/paper/2608.14667/) (2026)

## Abstract (arXiv)

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.