arXiv:2511.10354v1 Announce Type: new 
Abstract: Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse into structured Knowledge Graphs (KGs). This paper introduces ATR4CH (Adaptive Text-to-RDF for Cultural Heritage), a systematic five-step methodology for Large Language Model-based Knowledge Extraction from Cultural Heritage documents. We validate the methodology through a case study on authenticity assessment debates. Methodology - ATR4CH combines annotation models, ontological frameworks, and LLM-based extraction through iterative development: foundational analysis, annotation schema development, pipeline architecture, integration refinement, and comprehensive evaluation. We demonstrate the approach using Wikipedia articles about disputed items (documents, artifacts...), implementing a sequential pipeline with three LLMs (Claude Sonnet 3.7, Llama 3.3 70B, GPT-4o-mini). Findings - The methodology successfully extracts complex Cultural Heritage knowledge: 0.96-0.99 F1 for metadata extraction, 0.7-0.8 F1 for entity recognition, 0.65-0.75 F1 for hypothesis extraction, 0.95-0.97 for evidence extraction, and 0.62 G-EVAL for discourse representation. Smaller models performed competitively, enabling cost-effective deployment. Originality - This is the first systematic methodology for coordinating LLM-based extraction with Cultural Heritage ontologies. ATR4CH provides a replicable framework adaptable across CH domains and institutional resources. Research Limitations - The produced KG is limited to Wikipedia articles. While the results are encouraging, human oversight is necessary during post-processing. Practical Implications - ATR4CH enables Cultural Heritage institutions to systematically convert textual knowledge into queryable KGs, supporting automated metadata enrichment and knowledge discovery.

Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

<A HREF="https://www.businessinsider.com/meta-vibes-ai-internal-documents-show-daily-active-users-2025-11"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i40.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p40#a251118p40" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Pranav Dixit / <A HREF="https://www.businessinsider.com/">Business Insider</A>: 
<A HREF="https://www.businessinsider.com/meta-vibes-ai-internal-documents-show-daily-active-users-2025-11">Internal docs show Meta's Vibes AI video feed has about 2M DAUs as of November 9, up 1% from the previous week; ~52% of returning users prompted the AI</A>&nbsp; &mdash;&nbsp; - Internal documents show how Meta's new Vibes AI video feed is performing across its biggest markets.

تكشف الوثائق الداخلية أن خدمة الفيديو Vibes AI التابعة لشركة ميتا تضم حوالي 2 مليون مستخدم نشط يوميًا اعتبارًا من 9 نوفمبر، بزيادة قدرها 1% عن الأسبوع السابق. وقد تفاعل حوالي 52% من المستخدمين العائدين مع ميزة الذكاء الاصطناعي. تسلط هذه البيانات الضوء على زيادة التفاعل مع المحتوى المدعوم بالذكاء الاصطناعي من ميتا، مما يشير إلى اتجاه إيجابي في تفاعل المستخدمين واحتفاظهم.

Documentos internos revelan que el feed de video Vibes AI de Meta tiene aproximadamente 2 millones de usuarios activos diarios (UAD) a partir del 9 de noviembre, lo que representa un aumento del 1% respecto a la semana anterior. Aproximadamente el 52% de los usuarios que regresan interactuaron con la función de IA. Estos datos de rendimiento destacan el creciente compromiso con el contenido impulsado por IA de Meta, indicando una tendencia positiva en la interacción y retención de usuarios.

Des documents internes révèlent que le flux vidéo Vibes AI de Meta compte environ 2 millions d'utilisateurs actifs quotidiens (UAQ) au 9 novembre, soit une augmentation de 1 % par rapport à la semaine précédente. Environ 52 % des utilisateurs revenants ont interagi avec la fonctionnalité AI. Ces données de performance mettent en lumière l'engagement croissant envers le contenu alimenté par l'IA de Meta, indiquant une tendance positive dans l'interaction et la fidélisation des utilisateurs.

Internal documents reveal that Meta's Vibes AI video feed has approximately 2 million daily active users (DAUs) as of November 9, reflecting a 1% increase from the previous week. Notably, around 52% of returning users interacted with the AI feature. This performance data highlights the growing engagement with Meta's AI-driven content, indicating a positive trend in user interaction and retention.

Internal docs show Meta's Vibes AI video feed has about 2M DAUs as of November 9, up 1% from the previous week; ~52% of returning users prompted the AI (Pranav Dixit/Business Insider)

GlobalFoundries acquires AMF to become top silicon photonics foundry.
The post <a href="https://www.eetimes.com/gf-targets-1-billion-silicon-photonics-revenue-with-amf-acquisition/">GF Targets $1 Billion Silicon Photonics Revenue with AMF Acquisition</a> appeared first on <a href="https://www.eetimes.com">EE Times</a>.

استحوذت شركة GlobalFoundries على AMF، مما يضعها في موقع رائد في سوق الفوتونيك السيليكون. تهدف هذه الصفقة إلى تحقيق إيرادات تصل إلى مليار دولار من الفوتونيك السيليكون، وهي تقنية تدمج المكونات الضوئية مع الدوائر السيليكونية، مما يعزز سرعة وكفاءة نقل البيانات. تعكس هذه الخطوة التزام GlobalFoundries بتوسيع قدراتها في التقنيات المتقدمة وتلبية الطلب المتزايد على حلول الحوسبة عالية الأداء.

GlobalFoundries ha adquirido AMF, posicionándose como un jugador líder en el mercado de la fotónica de silicio. Esta adquisición tiene como objetivo generar 1.000 millones de dólares en ingresos de fotónica de silicio, una tecnología que integra componentes ópticos con circuitos de silicio, mejorando la velocidad y eficiencia de la transmisión de datos. Este movimiento refleja el compromiso de GlobalFoundries de expandir sus capacidades en tecnologías avanzadas y satisfacer la creciente demanda de soluciones informáticas de alto rendimiento.

GlobalFoundries a acquis AMF, se positionnant comme un acteur majeur sur le marché de la photonique silicium. Cette acquisition vise à générer 1 milliard de dollars de revenus provenant de la photonique silicium, une technologie qui intègre des composants optiques avec des circuits en silicium, améliorant ainsi la vitesse et l'efficacité de la transmission des données. Ce mouvement reflète l'engagement de GlobalFoundries à élargir ses capacités dans les technologies avancées et à répondre à la demande croissante de solutions informatiques haute performance.

GlobalFoundries has acquired AMF, positioning itself as a leading player in the silicon photonics market. This acquisition aims to generate $1 billion in revenue from silicon photonics, a technology that integrates optical components with silicon circuits, enhancing data transmission speeds and efficiency. The move reflects GlobalFoundries' commitment to expanding its capabilities in advanced technologies and meeting the growing demand for high-performance computing solutions.

GF Targets $1 Billion Silicon Photonics Revenue with AMF Acquisition

Black Friday is just two weeks away, and in the lead-up to the sales event, I've collected the best early Chromebook deals across major retailers.

يقترب يوم الجمعة السوداء بعد أسبوعين، وقد بدأت العروض المبكرة على أجهزة Chromebook تظهر في المتاجر الكبرى. يبرز هذا المقال أكثر من 20 عرضًا مبكرًا، مما يوفر للمستهلكين فرصة للتوفير في مشترياتهم خلال العطلات.

El Black Friday se acerca en dos semanas, y ya han comenzado a aparecer ofertas anticipadas en Chromebooks en los principales minoristas. Este artículo destaca más de 20 ventas anticipadas, brindando a los consumidores la oportunidad de ahorrar en sus compras navideñas.

Le Black Friday approche dans deux semaines, et des offres précoces sur les Chromebooks commencent à apparaître chez les principaux détaillants. Cet article met en avant plus de 20 ventes anticipées, offrant aux consommateurs l'occasion d'économiser sur leurs achats de vacances.

Black Friday is approaching in two weeks, and early Chromebook deals have started to appear across major retailers. This article highlights over 20 early sales, providing consumers with an opportunity to save on their holiday shopping.

Best early Black Friday Chromebook deals 2025: 20+ sales out early

Buy now, pay later firm says pay has risen by 60% with staff numbers mostly cut by natural attrition and tech investmentKlarna has claimed that AI-related savings have allowed the buy now, pay later company to increase staff salaries by nearly 60%, but hinted it could slash more jobs after nearly halving its workforce over the past three years.Chief executive Sebastian Siemiatkowski said headcount had dropped from 5,527 to 2,907 since 2022, mostly as a result of natural attrition, with departing staff replaced by technology rather than by new staff members. <a href="https://www.theguardian.com/business/2025/nov/18/buy-now-pay-later-klarna-ai-helped-halve-staff-boost-pay">Continue reading...</a>

أعلنت شركة كلارنا، المتخصصة في الدفع لاحقًا، عن زيادة بنسبة 60% في رواتب موظفيها، مشيرةً إلى أن هذه الزيادة جاءت نتيجة التوفير الناتج عن الاستثمارات في الذكاء الاصطناعي. وقد خفضت الشركة عدد موظفيها من 5,527 إلى 2,907 خلال السنوات الثلاث الماضية، وذلك بشكل رئيسي من خلال الاستقالة الطبيعية، حيث تم استبدال الموظفين المغادرين بالتكنولوجيا بدلاً من توظيف موظفين جدد. وأشار الرئيس التنفيذي سيباستيان سييمياتكوفسكي إلى أنه قد يتم تقليص المزيد من الوظائف مع استمرار الشركة في الاستفادة من التكنولوجيا لتعزيز الكفاءة.

Klarna, una empresa de 'compra ahora, paga después', ha informado de un aumento del 60% en los salarios de su personal, atribuyendo este incremento a los ahorros generados por inversiones en inteligencia artificial. La compañía ha reducido su plantilla de 5,527 a 2,907 en los últimos tres años, principalmente por medio de la baja natural, con la tecnología reemplazando a los empleados que se van en lugar de contratar nuevos. El CEO Sebastian Siemiatkowski indicó que podrían producirse más despidos a medida que la empresa continúe aprovechando la tecnología para mejorar la eficiencia.

Klarna, une entreprise de paiement différé, a annoncé une augmentation de 60 % des salaires de ses employés, attribuant cette hausse aux économies réalisées grâce aux investissements en IA. L'entreprise a réduit son effectif de 5 527 à 2 907 au cours des trois dernières années, principalement par attrition naturelle, la technologie remplaçant les employés partants au lieu d'embaucher de nouveaux. Le PDG Sebastian Siemiatkowski a indiqué que d'autres suppressions d'emplois pourraient se produire alors que l'entreprise continue d'exploiter la technologie pour améliorer son efficacité.

Klarna, a buy now, pay later company, has reported a 60% increase in staff salaries, attributing this rise to savings generated from AI investments. The company has reduced its workforce from 5,527 to 2,907 over the past three years, primarily through natural attrition, with technology replacing departing employees instead of hiring new staff. CEO Sebastian Siemiatkowski indicated that further job cuts could occur as the company continues to leverage technology to enhance efficiency.

Klarna says AI drive has helped halve staff numbers and boost pay

Evaluating Generative AI: A Novel Metric - Perceptual Diversity

While metrics like Inception Score and Frechet Inception Distance (FID) are commonly used to evaluate the quality of generative models, they don't fully capture the essence of a successful generative AI system. Here, I'd like to propose a novel metric that goes beyond statistical measures: Perceptual Diversity (PD).

What is Perceptual Diversity?

Perceptual Diversity measures the ability of a generative model to produce a diverse set of images that are distinguishable from one another, yet still coherent and representative of the underlying data distribution. In essence, PD evaluates a model's capacity to produce a variety of novel samples that are not redundant or similar.

Example: Generative AI for Architectural Design

Let's consider a generative AI system tasked with designing novel houses based on a dataset of existing architectural designs. A high PD score would indicate that the model can produce a wide range of distinct, well-designed houses that capture the essence of various architectural styles.

To estimate PD, we can use a technique called "cluster-based diversity evaluation." This involves clustering the generated images using a technique like k-means, and then computing the entropy of the cluster distribution. The higher the entropy, the more diverse the generated samples.

Example Results

Using a Generative Adversarial Network (GAN) model trained on a dataset of 1000 architectural designs, we obtained the following results:

<ul>
<li>Average Inception Score: 5.2</li>
<li>Average FID Score: 10.5</li>
<li>Average Perceptual Diversity (PD): 0.85</li>
</ul>

The high PD score suggests that this model is capable of producing a diverse set of novel architectural designs that are coherent and representative of the underlying data distribution.

Conclusion

Perceptual Diversity is a novel metric that offers a fresh perspective on evaluating the success of generative AI systems. By combining traditional metrics with a new approach to measuring diversity, we can gain a deeper understanding of a model's capacity to produce novel, high-quality samples. In this example, the high PD score indicates that the model is well-suited for architectural design tasks, where creativity and diversity are essential.




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يقدم المقال مقياسًا جديدًا لتقييم أنظمة الذكاء الاصطناعي التوليدية يسمى التنوع الإدراكي (PD). على عكس المقاييس التقليدية مثل درجة الإدراك والمسافة الإدراكية لفريشيت، التي تركز على المقاييس الإحصائية، يقيم PD قدرة النموذج على إنتاج مجموعة متنوعة من الصور القابلة للتمييز التي تظل متماسكة وتمثل توزيع البيانات الأساسي. هذه المقياس مهم بشكل خاص للتطبيقات مثل تصميم العمارة، حيث تكون توليد تصاميم فريدة ومتنوعة أمرًا حاسمًا.

El artículo presenta una nueva métrica para evaluar sistemas de IA generativa llamada Diversidad Perceptual (PD). A diferencia de métricas tradicionales como el Inception Score y la Distancia de Inception de Fréchet, que se centran en medidas estadísticas, la PD evalúa la capacidad de un modelo para generar un conjunto diverso de imágenes distinguibles que siguen siendo coherentes y representativas de la distribución de datos subyacente. Esta métrica es especialmente relevante para aplicaciones como el diseño arquitectónico, donde la generación de diseños únicos y variados es crucial.

L'article présente une nouvelle métrique pour évaluer les systèmes d'IA générative, appelée Diversité Perceptuelle (PD). Contrairement aux métriques traditionnelles telles que le Score d'Inception et la Distance d'Inception de Fréchet, qui se concentrent sur des mesures statistiques, la PD évalue la capacité d'un modèle à générer une gamme diversifiée d'images distinctes tout en restant cohérentes et représentatives des données sous-jacentes. Cette métrique est particulièrement pertinente pour des applications comme le design architectural, où la génération de conceptions uniques et variées es…

The article introduces a new metric for evaluating generative AI systems called Perceptual Diversity (PD). Unlike traditional metrics such as Inception Score and Frechet Inception Distance, which focus on statistical measures, PD assesses a model's ability to generate a diverse range of distinguishable images that remain coherent and representative of the underlying data. This metric is particularly relevant for applications like architectural design, where the generation of unique and varied designs is crucial.

**Evaluating Generative AI: A Novel Metric - Perceptual Dive

Black Friday is Nov. 28, but major retailers like Amazon, Best Buy, and Walmart are already slashing prices on top tech.

Best early Black Friday deals 2025: 55+ deals on TVs, laptops, streaming, and more

arXiv:2510.24021v2 Announce Type: replace 
Abstract: Knowledge distillation (KD) is a standard route to compress Large Language Models (LLMs) into compact students, yet most pipelines uniformly apply token-wise loss regardless of teacher confidence. This indiscriminate supervision amplifies noisy, high-entropy signals and is especially harmful under large teacher-student capacity gaps. We introduce SelecTKD, a plug-and-play Selective Token-Weighted distillation framework that shifts the focus from "how to measure divergence" to "where to apply learning". At each step, the student proposes tokens that are verified by the teacher through a robust propose-and-verify procedure with two variants: greedy Top-k and non-greedy Spec-k. Accepted tokens receive full loss, while rejected tokens are masked or down-weighted. This objective-agnostic design works with on- and off-policy data, induces an implicit curriculum quantified by Token Acceptance Rate (TAR), and stabilizes optimization. Across instruction following, mathematical reasoning, code generation, and a VLM setting, SelecTKD consistently improves strong baselines and achieves state-of-the-art results for small models without architectural changes or extra reference models.

SelecTKD: Selective Token-Weighted Knowledge Distillation for LLMs

arXiv:2511.13368v1 Announce Type: new 
Abstract: Large language models (LLMs) perform strongly across tasks and languages, yet how improvements in one task or language affect other tasks and languages and their combinations remains poorly understood. We conduct a controlled PEFT/LoRA study across multiple open-weight LLM families and sizes, treating task and language as transfer axes while conditioning on model family and size; we fine-tune each model on a single task-language source and measure transfer as the percentage-point change versus its baseline score when evaluated on all other task-language target pairs. We decompose transfer into (i) Matched-Task (Cross-Language), (ii) Matched-Language (Cross-Task), and (iii) Cross-Task (Cross-Language) regimes. We uncover two consistent general patterns. First, a pronounced on-task vs. off-task asymmetry: Matched-Task (Cross-Language) transfer is reliably positive, whereas off-task transfer often incurs collateral degradation. Second, a stable donor-recipient structure across languages and tasks (hub donors vs. brittle recipients). We outline implications for risk-aware fine-tuning and model specialisation.

Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning

arXiv:2511.11878v1 Announce Type: new 
Abstract: While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages, creating a critical barrier for others as simple translation fails to capture unique clinical and cultural nuances, such as endemic diseases. To address this, we introduce MedPT, the first large-scale, real-world corpus for Brazilian Portuguese, comprising 384,095 authentic question-answer pairs from patient-doctor interactions. The dataset underwent a meticulous multi-stage curation protocol, using a hybrid quantitative-qualitative analysis to filter noise and contextually enrich thousands of ambiguous queries. We further augmented the corpus via LLM-driven annotation, classifying questions into seven semantic types to capture user intent. Our analysis reveals its thematic breadth (3,200 topics) and unique linguistic properties, like the natural asymmetry in patient-doctor communication. To validate its utility, we benchmark a medical specialty routing task: fine-tuning a 1.7B parameter model achieves an outstanding 94\% F1-score on a 20-class setup. Furthermore, our qualitative error analysis shows misclassifications are not random but reflect genuine clinical ambiguities (e.g., between comorbid conditions), proving the dataset's deep semantic richness. We publicly release MedPT to foster the development of more equitable, accurate, and culturally-aware medical technologies for the Portuguese-speaking world.

Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

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