The Denario project: Deep knowledge AI agents for scientific discovery

By: Francisco Villaescusa-Navarro · Boris Bolliet · Pablo Villanueva-Domingo · Adrian E. Bayer · Aidan Acquah · Chetana Amancharla · Almog Barzilay-Siegal · Pablo Bermejo · Camille Bilodeau · Pablo C\'ardenas Ram\'irez · Miles Cranmer · Urbano L. Fran\c{c}a · 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 Taranc\'on-\'Alvarez · Ujjwal Tiwari · Matteo Viel · Digvijay Wadekar · Chi Wang · Bonny Y. Wang · Licong Xu · Yossi Yovel · Shuwen Yue · Wen-Han Zhou · Qiyao Zhu · Jiajun Zou · \'I\~nigo Zubeldia

3 November 2025 at 13:00

arXiv:2510.26887v1 Announce Type: new Abstract: 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.

Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence

cs.AI, q-bio.NC updates on arXiv.org

By: L\'ivia Dutra · Arthur Lorenzi · La\'is Berno · Franciany Campos · Karoline Biscardi · Kenneth Brown · Marcelo Viridiano · Frederico Belcavello · Ely Matos · Ol\'ivia Guaranha · Erik Santos · Sofia Reinach · Tiago Timponi Torrent

3 November 2025 at 13:00

arXiv:2510.26969v1 Announce Type: cross Abstract: We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification

cs.AI, q-bio.NC updates on arXiv.org

By: Flavio Corradini · Flavio Gerosa · Marco Gori · Carlo Lucheroni · Marco Piangerelli · Martina Zannotti

3 November 2025 at 13:00

arXiv:2410.22377v3 Announce Type: replace-cross Abstract: In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git providing additional interactive tools to further explore the presented findings.

Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates

cs.AI, q-bio.NC updates on arXiv.org

By: Tien Huu Do · Antoine Masquelier · Nae Eoun Lee · Jonathan Crowther

3 November 2025 at 13:00

arXiv:2507.23607v2 Announce Type: replace-cross Abstract: Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of trial outcomes. Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase. In this work, we propose a novel deep learning-based method to address this critical challenge. Our method, implemented as a neural network model, leverages pre-trained language models (PLMs) to capture the complexities and nuances of clinical documents, transforming them into expressive representations. These representations are then combined with encoded tabular features via an attention mechanism. To account for uncertainties in enrollment prediction, we enhance the model with a probabilistic layer based on the Gamma distribution, which enables range estimation. We apply the proposed model to predict clinical trial duration, assuming site-level enrollment follows a Poisson-Gamma process. We carry out extensive experiments on real-world clinical trial data, and show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial, outperforming established baseline models.

A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows

cs.AI, q-bio.NC updates on arXiv.org

By: Ant\'ia Dorado · Iv\'an Folgueira · Sof\'ia Mart\'in · Gonzalo Mart\'in · \'Alvaro Porto · Alejandro Ramos · John Wallace

3 November 2025 at 13:00

arXiv:2510.25935v2 Announce Type: replace-cross Abstract: CodeSight is an end-to-end system designed to anticipate deadline compliance in software development workflows. It captures development and deployment data directly from GitHub, transforming it into process mining logs for detailed analysis. From these logs, the system generates metrics and dashboards that provide actionable insights into PR activity patterns and workflow efficiency. Building on this structured representation, CodeSight employs an LSTM model that predicts remaining PR resolution times based on sequential activity traces and static features, enabling early identification of potential deadline breaches. In tests, the system demonstrates high precision and F1 scores in predicting deadline compliance, illustrating the value of integrating process mining with machine learning for proactive software project management.

On the limitation of evaluating machine unlearning using only a single training seed

cs.AI, q-bio.NC updates on arXiv.org

By: Jamie Lanyon · Axel Finke · Petros Andreou · Georgina Cosma

3 November 2025 at 13:00

arXiv:2510.26714v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to remove the influence of certain data points from a trained model without costly retraining. Most practical MU algorithms are only approximate and their performance can only be assessed empirically. Care must therefore be taken to make empirical comparisons as representative as possible. A common practice is to run the MU algorithm multiple times independently starting from the same trained model. In this work, we demonstrate that this practice can give highly non-representative results because -- even for the same architecture and same dataset -- some MU methods can be highly sensitive to the choice of random number seed used for model training. We therefore recommend that empirical comparisons of MU algorithms should also reflect the variability across different model training seeds.

International expert consensus on the clinical integration of circulating tumor cells in solid tumors

31 October 2025 at 18:00

Eur J Cancer. 2025 Dec 9;231:116050. doi: 10.1016/j.ejca.2025.116050. Epub 2025 Oct 20.

ABSTRACT

BACKGROUND: Circulating tumor cells (CTCs) are a versatile biomarker in solid tumors. Extensive research supports their clinical relevance and led to regulatory approval in breast, prostate, and colorectal cancers. However, clinical adoption remains limited mainly due to the lack of consensus and standardized technologies. Additionally, CTC research lacks unified direction. To address these gaps, an international expert panel was established to assess the current and future clinical utility of CTCs.

METHODS: A panel of 11 CTC experts identified key areas of controversy, informing a structured survey distributed to 55 international multidisciplinary experts. Consensus was predefined as ≥ 70 % agreement. Areas without consensus were discussed in a virtual meeting, leading to final statements on the clinical integration of CTCs.

RESULTS: Thirty-seven experts completed the survey. Consensus was reached on the clinical utility of CTCs for prognosis and treatment monitoring in metastatic breast (BC) and prostate (PC) cancers, including AR-V7 testing in metastatic castration-resistant PC for therapy selection. In other tumors, CTCs remain investigational. Experts agreed that while clinical utility is not yet established in early-stage disease, CTCs show promise in early BC, especially combined with cell-free DNA (cfDNA) for minimal residual disease detection. CellSearch® is currently the only platform with high-level evidence for clinical use, though emerging technologies are promising. Key challenges include improving detection sensitivity/specificity, standardizing workflows, generating robust data, and clinician education. Experts emphasized shifting from enumeration to phenotypic and molecular characterization, particularly for treatment guidance, and highlighted the complementary role of CTCs and cfDNA, advocating for integrated liquid biopsy approaches.

CONCLUSIONS: This consensus offers practical guidance for clinical integration of CTCs and outlines strategic research priorities to unlock their full potential in precision oncology.

PMID:41172567 | DOI:10.1016/j.ejca.2025.116050

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