arXiv:2511.17505v1 Announce Type: cross 
Abstract: To keep modern Radio Access Networks (RAN) running smoothly, operators need to spot the real-world triggers behind Service-Level Agreement (SLA) breaches well before customers feel them. We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold. We start by labeling network data: records linked to past SLA breaches are marked `abnormal', and everything else `normal'. Our model then learns the causal chain that turns normal behavior into a fault. In Monte Carlo tests the approach pinpoints the correct trigger sequence with high precision and scales to millions of data points without loss of speed. These results show that high-resolution, causally ordered insights can move fault management from reactive troubleshooting to proactive prevention.

تم تقديم خط أنابيب جديد يعتمد على الذكاء الاصطناعي/تعلم الآلة لتحسين تتبع الأعطال في شبكات الوصول اللاسلكي (RAN) من خلال تحديد المحفزات الحقيقية وراء انتهاكات اتفاقيات مستوى الخدمة (SLA) قبل أن تؤثر على العملاء. يقوم النموذج بتصنيف بيانات الشبكة على أنها 'غير طبيعية' أو 'طبيعية' ويتعلم السلسلة السببية التي تؤدي إلى الأعطال، محققًا دقة عالية في تحديد تسلسلات المحفزات خلال اختبارات مونت كارلو.

Se ha introducido un nuevo pipeline de IA/ML para mejorar el seguimiento de fallos en las Redes de Acceso Radio (RAN) al identificar los desencadenantes reales detrás de las violaciones de los Acuerdos de Nivel de Servicio (SLA) antes de que impacten a los clientes. El modelo etiqueta los datos de la red como 'anormales' o 'normales' y aprende la cadena causal que lleva a los fallos, logrando una alta precisión en la identificación de secuencias de desencadenamiento durante las pruebas de Monte Carlo.

Un nouveau pipeline d'IA/ML a été introduit pour améliorer le suivi des défauts dans les réseaux d'accès radio (RAN) en identifiant les déclencheurs réels des violations des accords de niveau de service (SLA) avant qu'elles n'impactent les clients. Le modèle étiquette les données réseau comme 'anormales' ou 'normales' et apprend la chaîne causale menant aux défauts, atteignant une haute précision dans l'identification des séquences de déclenchement lors de tests de Monte Carlo.

A new AI/ML pipeline has been introduced to enhance fault tracking in Radio Access Networks (RAN) by identifying real-world triggers behind Service-Level Agreement (SLA) breaches before they impact customers. The model labels network data as 'abnormal' or 'normal' and learns the causal chain leading to faults, achieving high precision in pinpointing trigger sequences during Monte Carlo tests.

Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks

arXiv:2511.19735v1 Announce Type: cross 
Abstract: Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Studies using real-world data (RWD), historically considered less reliable for establishing causality, are now recognized to be important for generating real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are being increasingly used throughout the drug development process, providing scalability and flexibility but also presenting challenges in interpretability and rigor that traditional statistics do not face. This Perspective argues that the future of evidence generation will not depend on RCTs versus RWD, or statistics versus AI/ML, but on their principled integration. To this end, a causal roadmap is needed to clarify inferential goals, make assumptions explicit, and ensure transparency about tradeoffs. We highlight key objectives of integrative evidence synthesis, including transporting RCT results to broader populations, embedding AI-assisted analyses within RCTs, designing hybrid controlled trials, and extending short-term RCTs with long-term RWD. We also outline future directions in privacy-preserving analytics, uncertainty quantification, and small-sample methods. By uniting statistical rigor with AI/ML innovation, integrative approaches can produce robust, transparent, and policy-relevant evidence, making them a key component of modern regulatory science.

تركز وجهة نظر جديدة حول توليد الأدلة على دمج التجارب السريرية العشوائية (RCTs) والبيانات الواقعية (RWD) والذكاء الاصطناعي وتعلم الآلة (AI/ML) والإحصائيات التقليدية. تتناول هذه المقاربة قيود التجارب السريرية العشوائية، مثل التكاليف العالية ومعايير الأهلية الصارمة، مع الاعتراف بأهمية البيانات الواقعية المتزايدة في توليد الأدلة الواقعية (RWE).

Una nueva perspectiva sobre la generación de evidencia enfatiza la integración de ensayos controlados aleatorios (ECA), datos del mundo real (DWR), inteligencia artificial y aprendizaje automático (IA/AA), y estadísticas tradicionales. Este enfoque aborda las limitaciones de los ECA, como los altos costos y los criterios de elegibilidad restrictivos, al mismo tiempo que reconoce la creciente importancia de los DWR en la generación de evidencia del mundo real (EWR).

Une nouvelle perspective sur la génération de preuves met l'accent sur l'intégration des essais contrôlés randomisés (ECR), des données du monde réel (DWR), de l'intelligence artificielle et de l'apprentissage automatique (IA/AA), ainsi que des statistiques traditionnelles. Cette approche répond aux limites des ECR, telles que les coûts élevés et les critères d'éligibilité restrictifs, tout en reconnaissant l'importance croissante des DWR dans la génération de preuves du monde réel (PWR).

A new perspective on evidence generation emphasizes the integration of randomized controlled trials (RCTs), real-world data (RWD), artificial intelligence and machine learning (AI/ML), and traditional statistics. This approach addresses the limitations of RCTs, such as high costs and restrictive eligibility, while recognizing the growing importance of RWD in generating real-world evidence (RWE).

Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis

arXiv:2511.20489v1 Announce Type: cross 
Abstract: Real-world AI/ML workflows often apply inference computations to feature vectors joined from multiple datasets. To avoid the redundant AI/ML computations caused by repeated data records in the join's output, factorized ML has been proposed to decompose ML computations into sub-computations to be executed on each normalized dataset. However, there is insufficient discussion on how factorized ML could impact AI/ML inference over multi-way joins. To address the limitations, we propose a novel declarative InferF system, focusing on the factorization of arbitrary inference workflows represented as analyzable expressions over the multi-way joins. We formalize our problem to flexibly push down partial factorized computations to qualified nodes in the join tree to minimize the overall inference computation and join costs and propose two algorithms to resolve the problem: (1) a greedy algorithm based on a per-node cost function that estimates the influence on overall latency if a subset of factorized computations is pushed to a node, and (2) a genetic algorithm for iteratively enumerating and evaluating promising factorization plans. We implement InferF on Velox, an open-sourced database engine from Meta, evaluate it on real-world datasets, observed up to 11.3x speedups, and systematically summarized the factors that determine when factorized ML can benefit AI/ML inference workflows.

تم اقتراح نظام جديد يسمى InferF لتحسين سير العمل في الذكاء الاصطناعي/تعلم الآلة من خلال تحليل حسابات الاستدلال على الانضمامات متعددة مجموعات البيانات. تهدف هذه الطريقة إلى تقليل الحسابات المكررة من خلال تفكيك مهام التعلم الآلي إلى حسابات فرعية يتم تنفيذها على مجموعات البيانات العادية، مما يعالج القيود المفروضة على الأساليب الحالية.

Se ha propuesto un nuevo sistema llamado InferF para mejorar los flujos de trabajo de IA/ML mediante la factorización de cálculos de inferencia sobre uniones de múltiples conjuntos de datos. Este enfoque busca minimizar los cálculos redundantes al descomponer las tareas de aprendizaje automático en subcálculos ejecutados en conjuntos de datos normalizados, abordando así las limitaciones de los métodos existentes.

Un nouveau système appelé InferF a été proposé pour améliorer les flux de travail en IA/ML en factorisant les calculs d'inférence sur des jointures multi-ensembles de données. Cette approche vise à minimiser les calculs redondants en décomposant les tâches d'apprentissage automatique en sous-calculs exécutés sur des ensembles de données normalisés, abordant ainsi les limites des méthodes existantes.

A novel system called InferF has been proposed to enhance AI/ML workflows by factorizing inference computations over multi-way joins of datasets. This approach aims to minimize redundant computations by decomposing machine learning tasks into sub-computations executed on normalized datasets, addressing the limitations of existing methods.

Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks

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