Tutorial
Open Access

Denario Project: Deep Knowledge Artificial Intelligence Agents for Scientific Discovery

Francisco Villaescusa-Navarro^1,2,*,†, Boris Bolliet^3,4,*, Pablo Villanueva-Domingo^5,*, Adrian E. Bayer^1,2, Aidan Acquah⁶, Chetana Amancharla⁷, Almog Barzilay-Siegal⁸, Pablo Bermejo^9,10,11, Camille Bilodeau¹² et al.

Pablo Cárdenas Ramírez^13,14,15, Miles Cranmer¹⁶, Urbano L. França^17,18, ChangHoon Hahn^19,20, Yan-Fei Jiang¹, Raul Jimenez ^21,22, Jun-Young Lee¹, Antonio Lerario²³, Osman Mamun¹³, Thomas Meier²⁴, Anupam A. Ojha^25,26, Pavlos Protopapas²⁷, Shimanto Roy¹², David N. Spergel¹, Pedro Tarancón-Álvarez^21,28, Ujjwal Tiwari⁷, Matteo Viel^{23,29,30,31,32}, Digvijay Wadekar^33,40, Chi Wang³⁴, Bonny Y. Wang³⁵, Licong Xu^4,36, Yossi Yovel^8,37, Shuwen Yue¹³, Wen-Han Zhou³⁸, Qiyao Zhu²⁵, Jiajun Zou³⁹, and Íñigo Zubeldia^4,16,36

¹Center for Computational Astrophysics, Flatiron Institute, New York, New York 10010, USA
²Department of Astrophysical Sciences, Princeton University, Princeton, New Jersey 08544, USA
³Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom
⁴Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom
⁵Computer Vision Center, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
⁶Big Data Institute, University of Oxford, Old Road Campus, Oxford OX3 7LF, United Kingdom
⁷Infosys Ltd, Electronics City, Hosur Road, Bengaluru, Karnataka, 560100, India
⁸School of Zoology, Faculty of Life Sciences, Tel-Aviv University, 6997801 Tel-Aviv, Israel
⁹Donostia International Physics Center, Paseo Manuel de Lardizabal 4, E-20018 San Sebastián, Spain
¹⁰Department of Applied Physics, University of the Basque Country, 20018 San Sebastián, Spain
¹¹Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA
¹²Chemical Engineering Department, University of Virginia, Wilsdorf Hall, Charlottesville, Virginia 22903, USA
¹³Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
¹⁴Ragon Institute of Mass General, MIT, and Harvard, 600 Main Street, Cambridge, Massachusetts 02139, USA
¹⁵Department of Microbiology, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA
¹⁶Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
¹⁷Boston Children's Hospital, Department of Anesthesiology, Critical Care and Pain Medicine, 300 Longwood Avenue, Bader 6, Boston, Massachusetts 02115, USA
¹⁸Harvard Medical School, 25 Shattuck Street, Boston, Massachusetts 02115, USA
¹⁹Department of Astronomy, The University of Texas at Austin, Austin, Texas 78712, USA
²⁰Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, Arizona 85721, USA
²¹Institut de Ciéncies del Cosmos, Universitat de Barcelona, Martí i Franqués 1, Barcelona, Spain
²²ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain
²³SISSA—International School for Advanced Studies, Via Bonomea 265, I-34136 Trieste, Italy
²⁴MCML—Munich Center for Machine Learning, LMU Munich, 80539 Munich, Germany
²⁵Center for Computational Biology, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA
²⁶Center for Computational Mathematics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA
²⁷Institute for Applied Computational Science, Harvard University, 150 Western Avenue, Massachusetts 02134, USA
²⁸Departament de Física Quántica i Astrofísica, Universitat de Barcelona, Barcelona, Spain
²⁹INFN—National Institute for Nuclear Physics, Via Valerio 2, 34127 Trieste, Italy
³⁰ICSC—Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy
³¹INAF—Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34143 Trieste, Italy
³²IFPU—Institute for Fundamental Physics of the Universe, Via Beirut 2, I-34151 Trieste, Italy
³³Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA
³⁴Google DeepMind, Platform 37, King's Cross Central, London N1C 4AG, United Kingdom
³⁵Department of Astronomy and Astrophysics, The University of Chicago, Chicago, Illinois 60637, USA
³⁶Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom
³⁷Sagol School of Neuroscience, Tel-Aviv University, 6997801 Tel-Aviv, Israel
³⁸Department of Earth and Planetary Science, The University of Tokyo, Tokyo 113-8654, Japan
³⁹SENSEx Lab, International School for Advanced Studies (SISSA), 34136 Trieste, Italy
⁴⁰Weinberg Institute, University of Texas at Austin, Austin, Texas 78712, USA

^*These authors contributed equally to this work.
^†Contact author: fvillaescusa@flatironinstitute.org

PDF

PRX Intelligence 1, 021001 – Published 1 October, 2026

DOI: https://doi.org/10.1103/kk55-gc95

Abstract

We present Denario, an Artificial Intelligence (AI) multiagent 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 a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea or carrying out near end-to-end scientific analysis using Cmbagent as a deep-research backend (end-to-end except for the novelty assessment, which is intentionally left for the user to inspect). 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 can also produce work that combines ideas from different disciplines, which we illustrate with a single ( $N = 1$ ) example: 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 reviewlike feedback. These papers were scored by domain experts on our team; we report these scores as an indicative self-assessment rather than an independent evaluation, and we flag the resulting conflict of interest explicitly. Most drafts were rated above the midpoint of a 0–10 scale, with several rated 8–9, indicating that the generated papers reach the level a good undergraduate or early graduate student could achieve after weeks or a few months of work. They should be regarded as first drafts that require expert development, not publication-ready papers. We also document an important failure mode in which, when the input data cannot be read, the system may silently generate synthetic data, which we flag explicitly as a key limitation. 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.

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (128)

A. F. Chalmers, What is This Thing Called Science?, 3rd ed. (Hackett Publishing, 1999).
R. D. King, K. E. Whelan, F. M. Jones, P. G. K. Reiser, C. H. Bryant, S. H. Muggleton, D. B. Kell, and S. G. Oliver, Functional genomic hypothesis generation and experimentation by a robot scientist, Nature (London) 427, 247 (2004).
K. Williams, E. Bilsland, A. Sparkes, W. Aubrey, M. Young, L. Soldatova, K. De Grave, J. Ramon, M. Clare, W. Sirawaraporn, S. Oliver, and R. King, Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases, J. R. Soc. Interface 12, 20141289 (2015).
J. R. Lloyd, D. Duvenaud, R. Grosse, Joshua B. Tenenbaum, and Z. Ghahramani, Automatic construction and natural-language description of nonparametric regression models, arXiv:1402.4304.
C. Lu, C. Lu, Robert T. Lange, J. Foerster, J. Clune, and D. Ha, The AI Scientist: Towards fully automated open-ended scientific discovery, arXiv:2408.06292.
Y. Yamada, Robert T. Lange, C. Lu, S. Hu, C. Lu, J. Foerster, J. Clune, and D. Ha, The AI Scientist-v2: Workshop-level automated scientific discovery via agentic tree search, arXiv:2504.08066.
J. Gottweis, W.-H. Weng, A. Daryin, T. Tu, A. Palepu, P. Sirkovic, A. Myaskovsky, F. Weissenberger, K. Rong, R. Tanno et al., Accelerating scientific discovery with Co-Scientist, Nature (London) 655, 487 (2026).
A. Moss, The AI Cosmologist I: An agentic system for automated data analysis, arXiv:2504.03424.
P. Tser Jern Kon, J. Liu, Q. Ding, Y. Qiu, Z. Yang, Y. Huang, J. Srinivasa, M. Lee, M. Chowdhury, and A. Chen, Curie: Toward rigorous and automated scientific experimentation with AI agents, arXiv:2502.16069.
S. Schmidgall, Y. Su, Z. Wang, X. Sun, J. Wu, X. Yu, J. Liu, M. Moor, Z. Liu, and E. Barsoum, Agent Laboratory: Using LLM agents as research assistants, arXiv:2501.04227.
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown, Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms, arXiv:1208.3719.
J. Baek, S. K. Jauhar, S. Cucerzan, and S. J. Hwang, ResearchAgent: Iterative research idea generation over scientific literature with large language models, arXiv:2404.07738.
J. T. Rapp, B. J. Bremer, and P. A. Romero, Self-driving laboratories to autonomously navigate the protein fitness landscape, Nat. Chem. Eng. 1, 97 (2024).
K. Swanson, W. Wu, N. L. Bulaong, J. E. Pak, and J. Zou, The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, bioRxiv 2024.11.11.623004 (2024).
D. Saeedi, D. Buckner, J. C. Aponte, and A. Aghazadeh, AstroAgents: A multi-agent AI for hypothesis generation from mass spectrometry data, arXiv:2503.23170.
A. Novikov, N. Vũ, M. Eisenberger, E. Dupont, P.-S. Huang, A. Z. Wagner, S. Shirobokov, B. Kozlovskii, F. J. R. Ruiz, A. Mehrabian, M. P. Kumar, A. See, S. Chaudhuri, G. Holland, A. Davies, S. Nowozin, P. Kohli, and M. Balog, AlphaEvolve: A coding agent for scientific and algorithmic discovery, arXiv:2506.13131.
E. Aygün, A. Belyaeva, G. Comanici, M. Coram, H. Cui, J. Garrison, R. J. A. Kast, C. Y. McLean, P. Norgaard, Z. Shamsi, D. Smalling, J. Thompson, S. Venugopalan, B. P. Williams, C. He, S. Martinson, M. Plomecka, L. Wei, Y. Zhou, Q.-Z. Zhu, et al., An AI system to help scientists write expert-level empirical software, arXiv:2509.06503.
R. T. Lange, Y. Imajuku, and E. Cetin, ShinkaEvolve: Towards open-ended and sample-efficient program evolution, arXiv:2509.19349.
Alireza Ghafarollahi and M. J. Buehler, SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning, arXiv:2409.05556.
M. J. Buehler, Agentic deep graph reasoning yields self-organizing knowledge networks, J. Mater. Res. 40, 2204 (2025).
S. Schmidgall and M. Moor, AgentRxiv: Towards collaborative autonomous research, arXiv:2503.18102.
N. J. Szymanski, B. Rendy, Y. Fei, R. E. Kumar, T. He, D. Milsted, M. J. McDermott, M. Gallant, E. D. Cubuk, A. Merchant, et al., An autonomous laboratory for the accelerated synthesis of novel materials, Nature (London) 624, 86 (2023).
J. Leeman, Y. Liu, J. Stiles, S. B. Lee, P. Bhatt, L. M. Schoop, and R. G. Palgrave, Challenges in high-throughput inorganic materials prediction and autonomous synthesis, PRX Energy 3, 011002 (2024).
S. Minaee, T. Mikolov, N. Nikzad, M. Chenaghlu, R. Socher, X. Amatriain, and J. Gao, Large language models: A survey, arXiv:2402.06196.
https://ag2.ai/.
https://www.langchain.com/LangGraph.
A. Laverick, K. Surrao, I. Zubeldia, B. Bolliet, M. Cranmer, A. Lewis, B. Sherwin, and J. Lesgourgues, Multi-agent system for cosmological parameter analysis, arXiv:2412.00431.
L. Xu, M. Sarkar, A. I. Lonappan, Í. Zubeldia, P. Villanueva-Domingo, S. Casas, C. Fidler, C. Amancharla, U. Tiwari, A. Bayer, et al., Open source planning & control system with language agents for autonomous scientific discovery, arXiv:2507.07257.
B. Bolliet and the CMBAGENT team, CMBAgent: Open-source multi-agent system for science, 2025, https://github.com/CMBAgents/cmbagent.
https://www.langchain.com/.
With more rounds, we often observe that the suggested plans become overly complex and ineffective.
As the context passed to the agents tends to grow during a session, a plan with more steps will generally lead to a more costly session.
https://github.com/AstroPilot-AI/DenarioExamplePapers.
C. Si, D. Yang, and T. Hashimoto, Can LLMs generate novel research ideas? A large-scale human study with 100+ NLP researchers, arXiv:2409.04109.
J. Li, K. Kuang, B. Wang, X. Li, F. Wu, J. Xiao, and L. Chen, Two heads are better than one: A simple exploration framework for efficient multi-agent reinforcement learning, in 37th Conference on Neural Information Processing Systems (2023).
H. Su, R. Chen, S. Tang, X. Zheng, J. Li, Z. Yin, W. Ouyang, and N. Dong, Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation (2024).
T. J. Jacobsson, A. Hultqvist, A. García-Fernández, A. Anand, A. Al-Ashouri, A. Hagfeldt, A. Crovetto, A. Abate, A. G. Ricciardulli, A. Vijayan, et al., An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles, Nat. Energy 7, 107 (2022).
https://www.semanticscholar.org/.
https://www.futurehouse.org/.
https://futurehouse.gitbook.io/futurehouse-cookbook/futurehouse-client.
X. Chen, M. Lin, N. Schärli, and D. Zhou, Teaching large language models to self-debug, arXiv:2304.05128.
For research notes or similar, the user may not find it useful to spend time and money adding citations. Given the cost, we tend to add citations only to the papers that have been reviewed and filtered by human experts.
This is in principle a hyperparameter. We have noticed that with current LLMs, including more than 10 figures tends to result in errors, omissions, etc.
https://unesdoc.unesco.org/ark:/48223/pf0000082946.
https://aaai.org/conference/aaai/aaai-26/keywords/.
https://journals.aas.org/keywords-2013/.
https://github.com/AstroPilot-AI/Denario.
https://pypi.org/project/denario/.
https://denario.readthedocs.io.
https://denario.readthedocs.io.
https://github.com/AstroPilot-AI/DenarioApp.
https://huggingface.co/spaces/astropilot-ai/Denario.
https://huggingface.co/spaces/astropilot-ai/Denario.
https://github.com/NeuroDiffGym/neurodiffeq.
M. W. M. G. Dissanayake and N. Phan-Thien, Neural-network-based approximations for solving partial differential equations, Commun. Numer. Methods Eng. 10, 195 (1994).
I. E. Lagaris, A. C. Likas, and D. G. Papageorgiou, Neural-network methods for boundary value problems with irregular boundaries, IEEE Trans. Neural Networks 11, 1041 (2000).
M. Raissi, P. Perdikaris, and G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378, 686 (2019).
W. Cai, C. Jiang, Y. Wang, and Y. Song, Structure-preserving algorithms for the two-dimensional sine-Gordon equation with Neumann boundary conditions, J. Comput. Phys. 395, 166 (2019).
M. Mattheakis, D. Sondak, A. S. Dogra, and P. Protopapas, Hamiltonian neural networks for solving equations of motion (2020).
J. Sirignano and K. Spiliopoulos, DGM: A deep learning algorithm for solving partial differential equations, J. Comput. Phys. 375, 1339 (2018).
Y. Bea, R. Jimenez, D. Mateos, S. Liu, P. Protopapas, P. Tarancón-Álvarez, and P. Tejerina-Pérez, Gravitational duals from equations of state, J. High Energy Phys. 07 (2024) 087.
P. Tarancón-Álvarez, P. Tejerina-Pérez, R. Jimenez, and P. Protopapas, Efficient PINNs: Multi-head unimodular regularization of the solutions space, Commun. Phys. 8, 335 (2025).
See Supplemental Material at http://link.aps.org/supplemental/10.1103/kk55-gc95 for full unedited AI-generated paper drafts across multiple scientific disciplines (Secs. S.1–S.13).
E. Bowell, K. Muinonen, and L. H. Wasserman, A public-domain asteroid orbit data base, in Asteroids, Comets, Meteors 1993, IAU Symposium, edited by A. Milani, Michel di Martino, and A. Cellino (1994), Vol. 160, pp. 477–481.
B. D. Warner, A. W. Harris, and P. Pravec, The asteroid lightcurve database, Icarus 202, 134 (2009).
J. Ďurech, V. Sidorin, and M. Kaasalainen, DAMIT: A database of asteroid models, Astron. Astrophys. 513, A46 (2010).
Minor planet physical properties catalogue (mp3c), https://mp3c.oca.eu, accessed 30 September 2025.
M. Mahlke, B. Carry, and P. A. Mattei, Asteroid taxonomy from cluster analysis of spectrometry and albedo, Astron. Astrophys. 665, A26 (2022).
R. Lenzen, M. Hartung, W. Brandner, G. Finger, N. Hubin, et al., NAOS-CONICA first on-sky results in a variety of observing modes, in Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, Proceedings of SPIE, edited by M. Iye and A. F. M. Moorwood (2003), Vol. 4841, pp. 944–952.
Ž. Ivezić, S. M. Kahn, J. A. Tyson, B. Abel, E. Acosta, R. Allsman, D. Alonso, Y. AlSayyad, S. F. Anderson, J. Andrew, et al., LSST: From science drivers to reference design and anticipated data products, Astrophys. J. 873, 111 (2019).
R. Gilmozzi and J. Spyromilio, The European Extremely Large Telescope (E-ELT), Messenger 127, 11 (2007).
https://www.cdc.gov/art/php/nass.
In other words, we generated papers from slightly different prompts.
LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration, GW231123: A binary black hole merger with total mass 190-265 $M$ $_{⊙}$ , Zenodo, 2025.
W. H. Organization et al., World malaria report 2024, World Health Organization, 2024.
V. M. Howick, A. J. C. Russell, T. Andrews, H. Heaton, A. J. Reid, K. Natarajan, H. Butungi, T. Metcalf, L. H. Verzier, J. C. Rayner, et al., The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle, Science 365, eaaw2619 (2019).
S. K. Dogga, J. C. Rop, J. Cudini, E. Farr, A. Dara, D. Ouologuem, A. A. Djimdé, A. M. Talman, and M. K. N. Lawniczak, A single cell atlas of sexual development in Plasmodium falciparum, Science 384, eadj4088 (2024).
L. McInnes, J. Healy, and J. Melville, UMAP: Uniform manifold approximation and projection for dimension reduction, arXiv:1802.03426.
F. A. Wolf, P. Angerer, and F. J. Theis, SCANPY: Large-scale single-cell gene expression data analysis, Genome Biol. 19, 15 (2018).
J. Cao, M. Spielmann, X. Qiu, X. Huang, D. M. Ibrahim, A. J. Hill, F. Zhang, S. Mundlos, L. Christiansen, F. J. Steemers, et al., The single-cell transcriptional landscape of mammalian organogenesis, Nature (London) 566, 496 (2019).
F. A. Wolf, F. K. Hamey, M. Plass, J. Solana, J. S. Dahlin, B. Göttgens, N. Rajewsky, L. Simon, and F. J. Theis, PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells, Genome Biol. 20, 59 (2019).
C. Aurrecoechea, J. Brestelli, B. P. Brunk, J. Dommer, S. Fischer, B. Gajria, X. Gao, A. Gingle, G. Grant, O. S. Harb, et al., PlasmoDB: A functional genomic database for malaria parasites, Nucl. Acids Res. 37, D539 (2009).
J. Alvarez-Jarreta, B. Amos, C. Aurrecoechea, S. Bah, M. Barba, A. Barreto, E. Y. Basenko, R. Belnap, A. Blevins, U. Böhme, et al., VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics resource center in 2023, Nucl. Acids Res. 52, D808 (2024).
K. Van den Berge, H. R. de Bézieux, K. Street, W. Saelens, R. Cannoodt, Y. Saeys, S. Dudoit, and L. Clement, Trajectory-based differential expression analysis for single-cell sequencing data, Nat. Commun. 11, 1201 (2020).
P. Schneider, S. E. Reece, B. C. L. Van Schaijk, T. Bousema, K. H. W. Lanke, C. S. J. Meaden, A. Gadalla, L. C. Ranford-Cartwright, and H. A. Babiker, Quantification of female and male Plasmodium falciparum gametocytes by reverse transcriptase quantitative PCR, Mol. Biochem. Parasitol. 199, 29 (2015).
W. G. Dixon, S. N. van der Veer, S. M. Ali, L. Laidlaw, R. J. B. Dobson, C. Sudlow, T. Chico, J. A. L. MacArthur, and A. Doherty, Charting a course for smartphones and wearables to transform population health research, J. Med. Internet Res. 25, e42449 (2023).
S. Huhn, M. Axt, H.-C. Gunga, M. A. Maggioni, S. Munga, D. Obor, A. Sié, V. Boudo, A. Bunker, R. Sauerborn, T. Bärnighausen, and S. Barteit, The impact of wearable technologies in health research: Scoping review, JMIR MHealth UHealth 10, e34384 (2022).
V. Vijayan, J. P. Connolly, J. Condell, N. McKelvey, and P. Gardiner, Review of wearable devices and data collection considerations for connected health, Sensors 21, 5589 (2021).
L. G. Roos and G. M. Slavich, Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations, Brain Behav. Immun. 113, 444 (2023).
B. C. K. Choi, A. W. P. Pak, J. C. L. Choi, and E. C. L. Choi, Daily step goal of 10 000 steps: A literature review, Clin. Invest. Med. 30, E146 (2007).
S. R. Small, S. Chan, R. Walmsley, L. von Fritsch, A. Acquah, G. Mertes, B. G. Feakins, A. Creagh, A. Strange, C. E. Matthews, D. A. Clifton, A. J. Price, S. Khalid, D. Bennett, and A. Doherty, Self-supervised machine learning to characterize step counts from wrist-worn accelerometers in the UK Biobank, Med. Sci. Sports Exercise 56, 1945 (2024).
C. M. Dobson, Protein folding and misfolding, Nature (London) 426, 884 (2003).
P. Eastman, R. Galvelis, R. P. Peláez, C. R. A. Abreu, S. E. Farr, E. Gallicchio, A. Gorenko, M. M. Henry, F. Hu, J. Huang, et al., OpenMM 8: Molecular dynamics simulation with machine learning potentials, J. Phys. Chem. B 128, 109 (2024).
K. Lindorff-Larsen, S. Piana, R. O. Dror, and D. E. Shaw, How fast-folding proteins fold, Science 334, 517 (2011).
V. A. Voelz, G. R. Bowman, K. Beauchamp, and V. S. Pande, Molecular simulation of ab initio protein folding for a millisecond folder NTL9 (1–39), J. Am. Chem. Soc. 132, 1526 (2010).
J. Monroe, M. Barry, A. DeStefano, P. Aydogan Gokturk, S. Jiao, D. Robinson-Brown, T. Webber, E. J. Crumlin, S. Han, and M. S. Shell, Water structure and properties at hydrophilic and hydrophobic surfaces, Annu. Rev. Chem. Biomol. Eng. 11, 523 (2020).
S. Jiao, L. E. Katz, and M. S. Shell, Inverse design of pore wall chemistry to control solute transport and selectivity, ACS Cent. Sci. 8, 1609 (2022).
N. Karamzadeh and F. Mozaffari, Structure and the dynamics of nano-confined water in the vicinity of functionalized graphene, Fluid Phase Equilib. 472, 160 (2018).
L. Hess, N. Nguyen, A. Dee, A. Gupta, Z. Kwon, and S. Yue, How surface functionalization controls confined electrolyte structure and dynamics at graphene interfaces, J. Phys. Chem. B 129, 10864 (2025).
F. Villaescusa-Navarro, D. Anglés-Alcázar, S. Genel, D. N. Spergel, R. S. Somerville, R. Dave, A. Pillepich, L. Hernquist, D. Nelson, P. Torrey, et al., The CAMELS Project: Cosmology and astrophysics with machine-learning simulations, Astrophys. J. 915, 71 (2021).
F. Villaescusa-Navarro, S. Genel, D. Anglés-Alcázar, L. A. Perez, P. Villanueva-Domingo, D. Wadekar, H. Shao, F. G. Mohammad, S. Hassan, E. Moser, et al., The CAMELS project: Public data release, Astrophys. J. Suppl. Series 265, 54 (2023).
L. A. Perez, S. Genel, F. Villaescusa-Navarro, R. S. Somerville, A. Gabrielpillai, D. Anglés-Alcázar, B. D. Wandelt, and L. Y. A. Yung, Constraining cosmology with machine learning and galaxy clustering: The CAMELS-SAM suite, Astrophys. J. 954, 11 (2023).
https://cosmobench.streamlit.app/.
N. Huang, R. Stiskalek, J.-Y. Lee, A. E. Bayer, C. C. Margossian, C. K. Jespersen, L. A. Perez, L. K. Saul, and F. Villaescusa-Navarro, CosmoBench: A multiscale, multiview, multitask cosmology benchmark for geometric deep learning, arXiv:2507.03707.
B. N. Khoromskij, $O (d log N)$ -quantics approximation of $N – d$ tensors in high-dimensional numerical modeling, Constr. Approx. 34, 257 (2011).
M. Ali and A. Nouy, Approximation theory of tree tensor networks: Tensorized univariate functions, Constr. Approx. 58, 463 (2023).
https://agents4science.stanford.edu/.
D. Astropilotai, P. Bermejo, B. Bolliet, F. Villaescusa-Navarro, and P. V. Domingo, QITT-enhanced multi-scale substructure analysis with learned topological embeddings for cosmological parameter estimation, in Open Conference of AI Agents for Science 2025 (2025).
P. Hosseinzadeh, G. Bhardwaj, V. Mulligan, M. Shortridge, T. Craven, F. Pardo-Avila, S. Rettie, et al., Comprehensive computational design of ordered peptide macrocycles, Science 358, 1461 (2017).
Q. Zhu, V. Mulligan, and D. Shasha, Heuristic energy-based cyclic peptide design, PLoS Comput. Biol. 21, e1012290 (2025).
J. Bochnak, M. Coste, and M.-F. Roy, Real Algebraic Geometry, Ergebnisse der Mathematik und ihrer Grenzgebiete (3) [Results in Mathematics and Related Areas (3)] (Springer, Berlin, 1998), Vol. 36.
P. Niyogi, S. Smale, and S. Weinberger, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. 39, 419 (2008).
Although we note that Denario contains the reviewer module that is designed to provide a referee-report-like as a proxy for performing evaluation.
B. Niu, Y. Song, K. Lian, Y. Shen, Y. Yao, K. Zhang, and T. Liu, Flow: Modularized agentic workflow automation, arXiv:2501.07834.
https://docs.massgen.ai/en/latest/.
D. J. H. Chung, Z. Gao, Y. Kvasiuk, T. Li, M. Münchmeyer, M. Rudolph, F. Sala, and S. C. Tadepalli, Theoretical physics benchmark (TPBench)—A dataset and study of AI reasoning capabilities in theoretical physics, Mach. Learn. Sci. Tech. 6, 030505 (2025).
C. Ye, S. Yuan, S. Cooray, S. Dillmann, I. L. V. Roque, D. Baron, P. Frank, S. Martin-Alvarez, N. Koblischke, F. J. Qu, D. Yang, R. Wechsler, and I. Ciuca, ReplicationBench: Can AI agents replicate astrophysics research papers?, arXiv:2510.24591.
S. Schmidgall and M. Moor, AgentRxiv: Towards collaborative autonomous research, arXiv:2503.18102.
M. Dauner and G. Socher, Energy costs of communicating with AI, Front. Commun. 10, 1572947 (2025).
R. Couillet, D. Trystram, and T. Ménissier, The submerged part of the AI-ceberg [perspectives], IEEE Signal Process. Mag. 39, 10 (2022).
I. Lakatos, in The Methodology of Scientific Research Programmes, edited by J. Worrall and G. Currie (Cambridge University Press, Cambridge, 1978).
W. van Orman Quine, Two dogmas of empiricism, in Can Theories be Refuted? Essays on the Duhem-Quine Thesis (Springer, 1976), pp. 41–64.
P. Feyerabend, Against Method: Outline of an Anarchistic Theory of Knowledge (Verso Books, 2020).
E. Brynjolfsson, The Turing trap: The promise & peril of human-like artificial intelligence, in Augmented Education in the Global Age (Routledge, 2023), pp. 103–116.
T. S. Kuhn and I. Hacking, The Structure of Scientific Revolutions (University of Chicago Press, Chicago, 1970), Vol. 2.
Allen Institute for Artificial Intelligence, AstaBench: Benchmarking AI agents for science, 2025, https://allenai.org/asta/bench.
X. Tang, Q. Jin, K. Zhu, T. Yuan, Y. Zhang, W. Zhou, M. Qu, Y. Zhao, J. Tang, Z. Zhang, A. Cohan, Z. Lu, and M. Gerstein, Risks of AI scientists: Prioritizing safeguarding over autonomy, arXiv:2402.04247.
https://denarioai.com.

PRX Intelligence 1, 021001– Published 1 October, 2026

Vol. 1, Iss. 2 — October 2026

Received 6 February 2026

DOI: https://doi.org/10.1103/kk55-gc95

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

PRX Intelligence

Reuse & Permissions

Export citation

Export citation

Denario Project: Deep Knowledge Artificial Intelligence Agents for Scientific Discovery

Abstract

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (128)

Outline

Information

Filter

Filter

Article Lookup

Enter a citation