MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams

Authors: Iker De la Iglesia, Johanna Ramirez-Romero, Jose Maria Villa-Gonzalez, Irune Urroz García, Ander Barrena, Aitziber Atutxa

Abstract: Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated… ▽ More Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot detect when a model reaches the correct diagnosis while grounding it in irrelevant, absent, or contradictory evidence. We introduce MIRA-Ev, a clinical argument mining benchmark built on Spanish Médico Interno Residente (MIR) licensing-exam cases, re-annotated by expert clinicians with span-level premises, claims, and directed support/attack relations, and released in parallel Spanish (native), English, and Basque versions, the first clinical argumentation resource in Basque. MIRA-Ev organizes evaluation into a three-tier task hierarchy: evidence sentence retrieval, argumentative component extraction, and relation classification. △ Less

Submitted 21 July, 2026; originally announced July 2026.

To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models

Authors: Ane G. Domingo-Aldama, Iker De La Iglesia, Maitane Urruela, Aitziber Atutxa, Ander Barrena

Abstract: BACKGROUND: Recent studies have shown that domain-adapted large language models (LLMs) do not consistently outperform general-purpose counterparts on standard medical benchmarks, raising questions about the need for specialized clinical adaptation. METHODS: We systematically compare general and clinical LLMs on a diverse set of multiple choice clinical question answering tasks in English and Spa… ▽ More BACKGROUND: Recent studies have shown that domain-adapted large language models (LLMs) do not consistently outperform general-purpose counterparts on standard medical benchmarks, raising questions about the need for specialized clinical adaptation. METHODS: We systematically compare general and clinical LLMs on a diverse set of multiple choice clinical question answering tasks in English and Spanish. We introduce a perturbation based evaluation benchmark that probes model robustness, instruction following, and sensitivity to adversarial variations. Our evaluation includes, one-step and two-step question transformations, multi prompt testing and instruction guided assessment. We analyze a range of state-of-the-art clinical models and their general-purpose counterparts, focusing on Llama 3.1-based models. Additionally, we introduce Marmoka, a family of lightweight 8B-parameter clinical LLMs for English and Spanish, developed via continual domain-adaptive pretraining on medical corpora and instructions. RESULTS: The experiments show that clinical LLMs do not consistently outperform their general purpose counterparts on English clinical tasks, even under the proposed perturbation based benchmark. However, for the Spanish subsets the proposed Marmoka models obtain better results compared to Llama. CONCLUSIONS: Our results show that, under current short-form MCQA benchmarks, clinical LLMs offer only marginal and unstable improvements over general-purpose models in English, suggesting that existing evaluation frameworks may be insufficient to capture genuine medical expertise. We further find that both general and clinical models exhibit substantial limitations in instruction following and strict output formatting. Finally, we demonstrate that robust medical LLMs can be successfully developed for low-resource languages such as Spanish, as evidenced by the Marmoka models. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Automating Early Disease Prediction Via Structured and Unstructured Clinical Data

Authors: Ane G Domingo-Aldama, Marcos Merino Prado, Alain García Olea, Josu Goikoetxea, Koldo Gojenola, Aitziber Atutxa

Abstract: This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techni… ▽ More This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techniques, we can efficiently identify relevant patient cohorts, enrich structured datasets with additional clinical variables, and generate high-quality labels without manual intervention. This approach addresses the frequent issue of missing or incomplete data in codified electronic health records (EHR), capturing clinically relevant information that is often underrepresented. We evaluate the methodology in the context of predicting atrial fibrillation (AF) progression, showing that predictive models trained on datasets enriched with discharge report information achieve higher accuracy and correlation with true outcomes compared to models trained solely on structured EHR data, while also surpassing traditional clinical scores. These results demonstrate that automating the integration of unstructured clinical text can streamline early prediction studies, improve data quality, and enhance the reliability of predictive models for clinical decision-making. △ Less

Submitted 30 March, 2026; originally announced March 2026.

IPsec based on Quantum Key Distribution: Adapting non-3GPP access to 5G Networks to the Quantum Era

Authors: Asier Atutxa, Ane Sanz, Eire Salegi, Gaizka González, Jasone Astorga, Eduardo Jacob

Abstract: The advent of quantum computing will pose great challenges to the current communication systems, requiring essential changes in the establishment of security associations in traditional architectures. In this context, the multi-technological and heterogeneous nature of 5G networks makes it a challenging scenario for the introduction of quantum communications. Specifically, 5G networks support the… ▽ More The advent of quantum computing will pose great challenges to the current communication systems, requiring essential changes in the establishment of security associations in traditional architectures. In this context, the multi-technological and heterogeneous nature of 5G networks makes it a challenging scenario for the introduction of quantum communications. Specifically, 5G networks support the unification of non-3GPP access technologies (i.e. Wi-Fi), which are secured through the IPsec protocol suite and the Non-3GPP Interworking Function (N3IWF) entity. These mechanisms leverage traditional public key cryptography and Diffie-Hellman key exchange mechanisms, which should be updated to quantum-safe standards. Therefore, in this paper we present the design and development of a Quantum Key Distribution (QKD) based non-3GPP access mechanism for 5G networks, integrating QKD keys with IPsec tunnel establishment. Besides, we also demonstrate the feasibility of the system by experimental validation in a testbed with commercial QKD equipment and an open-source 5G core implementation. Results show that the time required to complete the authentication and IPsec security association establishment is 4.62% faster than traditional cryptography PSK-based systems and 5.17% faster than the certificate-based system, while ensuring Information-Theoretic Security (ITS) of the QKD systems. △ Less

Submitted 25 March, 2026; originally announced March 2026.

QuLore: An Adaptive Security Framework to Extend Quantum-Safe Communications to Real-World Networks

Authors: Ane Sanz, Eire Salegi, Asier Atutxa, David Franco, Jasone Astorga, Eduardo Jacob

Abstract: The advent of quantum computing threatens classical cryptographic mechanisms, demanding new strategies for securing communication networks. Since real-world networks cannot be fully Quantum Key Distribution (QKD)-enabled due to infrastructure constraints, practical security solutions must support hybrid operation. This paper presents an adaptive security framework that enables quantum-safe communi… ▽ More The advent of quantum computing threatens classical cryptographic mechanisms, demanding new strategies for securing communication networks. Since real-world networks cannot be fully Quantum Key Distribution (QKD)-enabled due to infrastructure constraints, practical security solutions must support hybrid operation. This paper presents an adaptive security framework that enables quantum-safe communications across real-world heterogeneous networks by combining QKD and Post-Quantum Cryptography (PQC). Building upon a hierarchical key management architecture with Virtual Key Management Systems (vKMS) and a centralized Quantum Security Controller (QuSeC), the framework dynamically assigns security levels based on node capabilities. By transitioning between pure QKD, hybrid, and PQC modes, it ensures end-to-end quantum-safe protection regardless of the underlying node capabilities. The framework has been implemented and validated on a Kubernetes-based containerized testbed, demonstrating robust operation and performance across all scenarios. Results highlight its potential to support the gradual integration of quantum-safe technologies into existing infrastructures, paving the way toward fully quantum-safe communication networks. △ Less

Submitted 23 February, 2026; v1 submitted 27 November, 2025; originally announced November 2025.

Toward quantum-safe scalable networks: an open, standards-aware key management framework

Authors: Ane Sanz, Asier Atutxa, David Franco, Jasone Astorga, Eduardo Jacob, Diego López

Abstract: With the advent of quantum computing, the increasing threats to security poses a great challenge to communication networks. Recent innovations in this field resulted in promising technologies such as Quantum Key Distribution (QKD), which enables the generation of unconditionally secure keys, establishing secure communications between remote nodes. Additionally, QKD networks enable the interconnect… ▽ More With the advent of quantum computing, the increasing threats to security poses a great challenge to communication networks. Recent innovations in this field resulted in promising technologies such as Quantum Key Distribution (QKD), which enables the generation of unconditionally secure keys, establishing secure communications between remote nodes. Additionally, QKD networks enable the interconnection of multinode architectures, extending the point-to-point nature of QKD. However, due to the limitations of the current state of technology, the scalability of QKD networks remains a challenge toward feasible implementations. When it comes to long-distance implementations, trusted relay nodes partially solve the distance issue through the forwarding of the distributed keys, allowing applications that do not have a direct QKD link to securely share key material. Even though the relay procedure itself has been extensively studied, the establishment of the relaying node path still lacks a solution. This paper proposes an innovative network architecture that solves the challenges of Key Management System (KMS) identification, relay path discovery, and scalability of QKD networks by integrating Software-Defined Networking (SDN) principles, and establishing high-level virtual KMSs (vKMS) in each node and creating a new entity called the Quantum Security Controller (QuSeC). The vKMS serves the end-user key requests, managing the multiple KMSs within the node and abstracting the user from discovering the correct KMS. Additionally, based on the high-level view of the network topology and status, the QuSeC serves the path discovery requests from vKMSs, computing the end-to-end (E2E) relay path and applying security policies. The paper also provides a security analysis of the proposal, identifying the security levels of the architecture and analyzing the core networking security properties. △ Less

Submitted 15 December, 2025; v1 submitted 11 September, 2025; originally announced September 2025.

ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality

Authors: Adrián Cuadrón, Aimar Sagasti, Maitane Urruela, Iker De la Iglesia, Ane G Domingo-Aldama, Aitziber Atutxa, Josu Goikoetxea, Ander Barrena

Abstract: This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate… ▽ More This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality. △ Less

Submitted 15 June, 2025; originally announced June 2025.

SareQuant: Towards a quantum-based communication network

Authors: Ane Sanz, David Franco, Asier Atutxa, Jasone Astorga, Eduardo Jacob

Abstract: This paper presents the SareQuant project, which aims to evolve the Basque NREN (National Research and Education Networks) into a quantum-based communication infrastructure. SareQuant focuses on the network design and on the integration of quantum technologies into real-world scenarios and applications. Therefore, this paper provides insights into the opportunities and challenges regarding the int… ▽ More This paper presents the SareQuant project, which aims to evolve the Basque NREN (National Research and Education Networks) into a quantum-based communication infrastructure. SareQuant focuses on the network design and on the integration of quantum technologies into real-world scenarios and applications. Therefore, this paper provides insights into the opportunities and challenges regarding the integration of quantum technologies, thus paving the way for a secure and advanced Quantum Internet. △ Less

Submitted 16 December, 2024; originally announced December 2024.

Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model

Authors: Ali Hamza, Aizea Lojo, Adrian Núñez-Marcos, Aitziber Atutxa

Abstract: This paper introduces Ali-AUG, a novel single-step diffusion model for efficient labeled data augmentation in industrial applications. Our method addresses the challenge of limited labeled data by generating synthetic, labeled images with precise feature insertion. Ali-AUG utilizes a stable diffusion architecture enhanced with skip connections and LoRA modules to efficiently integrate masks and im… ▽ More This paper introduces Ali-AUG, a novel single-step diffusion model for efficient labeled data augmentation in industrial applications. Our method addresses the challenge of limited labeled data by generating synthetic, labeled images with precise feature insertion. Ali-AUG utilizes a stable diffusion architecture enhanced with skip connections and LoRA modules to efficiently integrate masks and images, ensuring accurate feature placement without affecting unrelated image content. Experimental validation across various industrial datasets demonstrates Ali-AUG's superiority in generating high-quality, defect-enhanced images while maintaining rapid single-step inference. By offering precise control over feature insertion and minimizing required training steps, our technique significantly enhances data augmentation capabilities, providing a powerful tool for improving the performance of deep learning models in scenarios with limited labeled data. Ali-AUG is especially useful for use cases like defective product image generation to train AI-based models to improve their ability to detect defects in manufacturing processes. Using different data preparation strategies, including Classification Accuracy Score (CAS) and Naive Augmentation Score (NAS), we show that Ali-AUG improves model performance by 31% compared to other augmentation methods and by 45% compared to models without data augmentation. Notably, Ali-AUG reduces training time by 32% and supports both paired and unpaired datasets, enhancing flexibility in data preparation. △ Less

Submitted 24 October, 2024; originally announced October 2024.

Preliminary approaches towards the integration of TSN communications into the NFV architectural framework

Authors: Jorge Sasiain, Asier Atutxa, David Franco, Jasone Astorga, Eduardo Jacob

Abstract: This paper presents a preliminary architecture for the integration of Time-Sensitive Networking (TSN) communications into the Network Functions Virtualization (NFV) architectural framework. Synergies between functional blocks and constructs of NFV, and components of TSN networks, are investigated in order to arrive at an integrated architecture. Additionally, mechanisms and configuration procedure… ▽ More This paper presents a preliminary architecture for the integration of Time-Sensitive Networking (TSN) communications into the Network Functions Virtualization (NFV) architectural framework. Synergies between functional blocks and constructs of NFV, and components of TSN networks, are investigated in order to arrive at an integrated architecture. Additionally, mechanisms and configuration procedures to enable TSN-compliant, real-time, and virtualized end stations under the NFV framework are explored. △ Less

Submitted 22 July, 2024; originally announced July 2024.

Explanatory Argument Extraction of Correct Answers in Resident Medical Exams

Authors: Iakes Goenaga, Aitziber Atutxa, Koldo Gojenola, Maite Oronoz, Rodrigo Agerri

Abstract: Developing the required technology to assist medical experts in their everyday activities is currently a hot topic in the Artificial Intelligence research field. Thus, a number of large language models (LLMs) and automated benchmarks have recently been proposed with the aim of facilitating information extraction in Evidence-Based Medicine (EBM) using natural language as a tool for mediating in hum… ▽ More Developing the required technology to assist medical experts in their everyday activities is currently a hot topic in the Artificial Intelligence research field. Thus, a number of large language models (LLMs) and automated benchmarks have recently been proposed with the aim of facilitating information extraction in Evidence-Based Medicine (EBM) using natural language as a tool for mediating in human-AI interaction. The most representative benchmarks are limited to either multiple-choice or long-form answers and are available only in English. In order to address these shortcomings, in this paper we present a new dataset which, unlike previous work: (i) includes not only explanatory arguments for the correct answer, but also arguments to reason why the incorrect answers are not correct; (ii) the explanations are written originally by medical doctors to answer questions from the Spanish Residency Medical Exams. Furthermore, this new benchmark allows us to setup a novel extractive task which consists of identifying the explanation of the correct answer written by medical doctors. An additional benefit of our setting is that we can leverage the extractive QA paradigm to automatically evaluate performance of LLMs without resorting to costly manual evaluation by medical experts. Comprehensive experimentation with language models for Spanish shows that sometimes multilingual models fare better than monolingual ones, even outperforming models which have been adapted to the medical domain. Furthermore, results across the monolingual models are mixed, with supposedly smaller and inferior models performing competitively. In any case, the obtained results show that our novel dataset and approach can be an effective technique to help medical practitioners in identifying relevant evidence-based explanations for medical questions. △ Less

Submitted 1 December, 2023; originally announced December 2023.

EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing

Authors: Iker de la Iglesia, Aitziber Atutxa, Koldo Gojenola, Ander Barrena

Abstract: The utilization of clinical reports for various secondary purposes, including health research and treatment monitoring, is crucial for enhancing patient care. Natural Language Processing (NLP) tools have emerged as valuable assets for extracting and processing relevant information from these reports. However, the availability of specialized language models for the clinical domain in Spanish has be… ▽ More The utilization of clinical reports for various secondary purposes, including health research and treatment monitoring, is crucial for enhancing patient care. Natural Language Processing (NLP) tools have emerged as valuable assets for extracting and processing relevant information from these reports. However, the availability of specialized language models for the clinical domain in Spanish has been limited. In this paper, we introduce EriBERTa, a bilingual domain-specific language model pre-trained on extensive medical and clinical corpora. We demonstrate that EriBERTa outperforms previous Spanish language models in the clinical domain, showcasing its superior capabilities in understanding medical texts and extracting meaningful information. Moreover, EriBERTa exhibits promising transfer learning abilities, allowing for knowledge transfer from one language to another. This aspect is particularly beneficial given the scarcity of Spanish clinical data. △ Less

Submitted 12 June, 2023; originally announced June 2023.

HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine

Authors: Rodrigo Agerri, Iñigo Alonso, Aitziber Atutxa, Ander Berrondo, Ainara Estarrona, Iker Garcia-Ferrero, Iakes Goenaga, Koldo Gojenola, Maite Oronoz, Igor Perez-Tejedor, German Rigau, Anar Yeginbergenova

Abstract: Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contribute… ▽ More Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contributed to the decision; making use of additional knowledge (e.g. expert evidence) which might not be part of the prediction process; and providing evidence supporting negative hypothesis. Finally, the system needs to formulate the explanation in a clearly interpretable, and possibly convincing, way. Given these considerations, ANTIDOTE fosters an integrated vision of explainable AI, where low-level characteristics of the deep learning process are combined with higher level schemes proper of the human argumentation capacity. ANTIDOTE will exploit cross-disciplinary competences in deep learning and argumentation to support a broader and innovative view of explainable AI, where the need for high-quality explanations for clinical cases deliberation is critical. As a first result of the project, we publish the Antidote CasiMedicos dataset to facilitate research on explainable AI in general, and argumentation in the medical domain in particular. △ Less

Submitted 9 June, 2023; originally announced June 2023.

Towards integrating hardware Data Plane acceleration in Network Functions Virtualization

Authors: David Franco, Asier Atutxa, Jorge Sasiain, Eder Ollora, Marivi Higuero, Jasone Astorga, Eduardo Jacob

Abstract: This paper proposes a framework for integrating data plane (DP) acceleration within the Network Functions Virtualization (NFV) architecture. Data plane programming (DPP) proves to be beneficial for NFV environments, as it provides full packet forwarding flexibility through the use of self-designed algorithms. Additionally, DPP provides high-performance networking, as the DP can be configured to ex… ▽ More This paper proposes a framework for integrating data plane (DP) acceleration within the Network Functions Virtualization (NFV) architecture. Data plane programming (DPP) proves to be beneficial for NFV environments, as it provides full packet forwarding flexibility through the use of self-designed algorithms. Additionally, DPP provides high-performance networking, as the DP can be configured to execute specific functions on dedicated hardware. We present an integration of the DP acceleration within the ETSI NFV architecture that leverages custom DP functions implemented in hardware switches using P4 language. Besides, OpenStack and Kubernetes are used as Virtualized Infrastructure Managers (VIMs) and Open Source MANO (OSM) as the Management and Orchestration (MANO) element. △ Less

Submitted 21 March, 2022; originally announced March 2022.