arXiv:2512.00979v1 Announce Type: new 
Abstract: Principal Component Analysis (PCA) and K-means constitute fundamental techniques in multivariate analysis. Although they are frequently applied independently or sequentially to cluster observations, the relationship between them, especially when K-means is used to cluster variables rather than observations, has been scarcely explored. This study seeks to address this gap by proposing an innovative method that analyzes the relationship between clusters of variables obtained by applying K-means on transposed data and the principal components of PCA. Our approach involves applying PCA to the original data and K-means to the transposed data set, where the original variables are converted into observations. The contribution of each variable cluster to each principal component is then quantified using measures based on variable loadings. This process provides a tool to explore and understand the clustering of variables and how such clusters contribute to the principal dimensions of variation identified by PCA.

تقدم دراسة جديدة طريقة مبتكرة تستكشف العلاقة بين تجميع K-means المطبق على البيانات المنقولة وتحليل المكونات الرئيسية (PCA). هذه الطريقة تقيس مساهمة مجموعات المتغيرات في المكونات الرئيسية، مما يعالج فجوة في فهم تقنيات تجميع المتغيرات.

Un nuevo estudio presenta un método innovador que explora la relación entre el agrupamiento K-means aplicado a datos transpuestos y el Análisis de Componentes Principales (PCA). Este enfoque cuantifica la contribución de los clústeres de variables a los componentes principales, abordando una brecha en la comprensión de las técnicas de agrupamiento de variables.

Une nouvelle étude présente une méthode innovante qui explore la relation entre le clustering K-means appliqué à des données transposées et l'analyse en composantes principales (ACP). Cette approche quantifie la contribution des clusters de variables aux composantes principales, comblant ainsi une lacune dans la compréhension des techniques de clustering de variables.

A new study introduces an innovative method that explores the relationship between K-means clustering applied to transposed data and Principal Component Analysis (PCA). This approach quantifies the contribution of variable clusters to principal components, addressing a gap in the understanding of variable clustering techniques.

An Approach to Variable Clustering: K-means in Transposed Data and its Relationship with Principal Component Analysis

arXiv:2601.08331v1 Announce Type: new 
Abstract: Understanding and controlling the behavior of large language models (LLMs) is an increasingly important topic in multilingual NLP. Beyond prompting or fine-tuning, , i.e.,~manipulating internal representations during inference, has emerged as a more efficient and interpretable technique for adapting models to a target language. Yet, no dedicated benchmarks or evaluation protocols exist to quantify the effectiveness of steering techniques. We introduce CLaS-Bench, a lightweight parallel-question benchmark for evaluating language-forcing behavior in LLMs across 32 languages, enabling systematic evaluation of multilingual steering methods. We evaluate a broad array of steering techniques, including residual-stream DiffMean interventions, probe-derived directions, language-specific neurons, PCA/LDA vectors, Sparse Autoencoders, and prompting baselines. Steering performance is measured along two axes: language control and semantic relevance, combined into a single harmonic-mean steering score. We find that across languages simple residual-based DiffMean method consistently outperforms all other methods. Moreover, a layer-wise analysis reveals that language-specific structure emerges predominantly in later layers and steering directions cluster based on language family. CLaS-Bench is the first standardized benchmark for multilingual steering, enabling both rigorous scientific analysis of language representations and practical evaluation of steering as a low-cost adaptation alternative.

يمثل تقديم CLaS-Bench تقدمًا كبيرًا في تقييم نماذج اللغة الكبيرة (LLMs)، حيث يوفر معيارًا للأسئلة الموازية لتقييم تقنيات التوجيه متعددة اللغات عبر 32 لغة. يهدف هذا المعيار إلى قياس فعالية مجموعة متنوعة من أساليب التوجيه، بما في ذلك تدخلات DiffMean في التدفق المتبقي والعصبونات الخاصة باللغة.

La introducción de CLaS-Bench marca un avance significativo en la evaluación de los grandes modelos de lenguaje (LLMs), proporcionando un benchmark de preguntas paralelas para evaluar técnicas de dirección multilingües en 32 idiomas. Este benchmark tiene como objetivo cuantificar la efectividad de varios métodos de dirección, incluyendo intervenciones DiffMean de flujo residual y neuronas específicas de idioma.

L'introduction de CLaS-Bench représente une avancée significative dans l'évaluation des grands modèles de langage (LLMs), fournissant un benchmark de questions parallèles pour évaluer les techniques de pilotage multilingues dans 32 langues. Ce benchmark vise à quantifier l'efficacité de diverses méthodes de pilotage, y compris les interventions DiffMean en flux résiduel et les neurones spécifiques à la langue.

The introduction of CLaS-Bench marks a significant advancement in the evaluation of large language models (LLMs), providing a parallel-question benchmark for assessing multilingual steering techniques across 32 languages. This benchmark aims to quantify the effectiveness of various steering methods, including residual-stream DiffMean interventions and language-specific neurons.

CLaS-Bench: A Cross-Lingual Alignment and Steering Benchmark

arXiv:2601.08733v1 Announce Type: new 
Abstract: Artificial Intelligence (AI) systems have shown good success at classifying. However, the lack of explainability is a true and significant challenge, especially in high-stakes domains, such as health and finance, where understanding is paramount. We propose a new solution to this challenge: an explainable AI framework based on our comparative study with Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs). We leverage principles of quantum computing within classical machine learning to provide substantive transparency around decision-making. The design involves training both models on a binarised and dimensionally reduced MNIST dataset, where Principal Component Analysis (PCA) is applied for preprocessing. For interpretability, we employ gradient-based saliency maps in QBMs and SHAP (SHapley Additive exPlanations) in CBMs to evaluate feature attributions.QBMs deploy hybrid quantum-classical circuits with strongly entangling layers, allowing for richer latent representations, whereas CBMs serve as a classical baseline that utilises contrastive divergence. Along the way, we found that QBMs outperformed CBMs on classification accuracy (83.5% vs. 54%) and had more concentrated distributions in feature attributions as quantified by entropy (1.27 vs. 1.39). In other words, QBMs not only produced better predictive performance than CBMs, but they also provided clearer identification of "active ingredient" or the most important features behind model predictions. To conclude, our results illustrate that quantum-classical hybrid models can display improvements in both accuracy and interpretability, which leads us toward more trustworthy and explainable AI systems.

تم اقتراح نهج جديد للذكاء الاصطناعي القابل للتفسير، مستفيدًا من آلات بولتزمان الكمومية (QBMs) وآلات بولتزمان الكلاسيكية (CBMs) لتعزيز الشفافية في اتخاذ القرار. يستخدم هذا الإطار خرائط السالينسي المعتمدة على التدرجات وSHAP لتقييم نسبة الميزات، مما يعالج التحدي الحاسم المتمثل في القابلية للتفسير في المجالات عالية المخاطر مثل الصحة والمالية.

Se ha propuesto un nuevo enfoque para la inteligencia artificial (IA) explicable, aprovechando las Máquinas de Boltzmann Cuánticas (QBMs) y las Máquinas de Boltzmann Clásicas (CBMs) para mejorar la transparencia en la toma de decisiones. Este marco utiliza mapas de saliencia basados en gradientes y SHAP para la atribución de características, abordando el desafío crítico de la explicabilidad en dominios de alto riesgo como la salud y las finanzas.

Une nouvelle approche de l'intelligence artificielle (IA) explicable a été proposée, tirant parti des machines de Boltzmann quantiques (QBMs) et des machines de Boltzmann classiques (CBMs) pour améliorer la transparence des décisions. Ce cadre utilise des cartes de saillance basées sur le gradient et SHAP pour l'attribution des caractéristiques, abordant le défi critique de l'explicabilité dans des domaines à enjeux élevés comme la santé et la finance.

A novel approach to explainable artificial intelligence (AI) has been proposed, leveraging Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs) to enhance decision-making transparency. This framework utilizes gradient-based saliency maps and SHAP for feature attribution, addressing the critical challenge of explainability in high-stakes domains like healthcare and finance.

An Approach to Variable Clustering: K-means in Transposed Data and its Relationship with Principal Component Analysis

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