The company has been quietly changing how it talks about who its internet customers will be before it announced a big name change last week.

قامت أمازون مؤخرًا بإعادة تسمية شبكة الأقمار الصناعية الخاصة بها، مما غير رسالتها بشأن من سيكون عملاء الإنترنت. يأتي هذا التغيير في الوقت الذي تبتعد فيه الشركة عن الترويج للقدرة على تحمل التكاليف كنقطة بيع رئيسية.

Amazon ha rebrandeado recientemente su red de satélites, cambiando su mensaje sobre quiénes serán sus clientes de internet. Este cambio se produce mientras la compañía se aleja de promover la asequibilidad como un punto de venta clave.

Amazon a récemment rebrandé son réseau satellite, modifiant son discours concernant les clients potentiels d'internet. Ce changement intervient alors que l'entreprise abandonne la promotion de l'accessibilité comme argument de vente principal.

Amazon has recently rebranded its satellite network, shifting its messaging regarding potential internet customers. This change comes as the company moves away from promoting affordability as a key selling point.

Amazon satellite network gets a rebrand — and drops its affordability pitch

arXiv:1906.07172v5 Announce Type: replace-cross 
Abstract: Equivariant neural networks are a class of neural networks designed to preserve symmetries inherent in the data. In this paper, we introduce a general method for modifying a neural network to enforce equivariance, a process we refer to as equivarification. We further show that group convolutional neural networks (G-CNNs) arise as a special case of our framework.

Equivariant neural networks and equivarification

arXiv:2311.13745v4 Announce Type: replace-cross 
Abstract: Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpoint, a number of recent works have studied the iteration complexity of sampling, assuming access to an accurate diffusion model. In this work, we focus on understanding the sample complexity of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an exponential improvement in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters.

Improved Sample Complexity Bounds for Diffusion Model Training

arXiv:2410.22995v2 Announce Type: replace-cross 
Abstract: A hallmark of advanced artificial intelligence is the capacity to progress from passive visual perception to the strategic modification of visual information to facilitate complex reasoning. This advanced capability, however, remains critically underdeveloped in current Large Multi-modal Models (LMMs). The deficiency is often masked by evaluation metrics that prioritize final-answer accuracy, creating an illusion of competence where genuine reasoning is absent. Using the domain of geometric problem-solving as a precise instrument, we probe this issue through tasks that require constructing visual aids. To this end, we introduce \textbf{VisAidMath}, a challenging benchmark, and our novel Three-Layered Funnel Evaluation Framework. This framework moves beyond simple accuracy (ACCU) to scrutinize the generation of valid visual aids (PVA) and the soundness of subsequent reasoning steps (SPRS). Our extensive experiments on state-of-the-art models, including Doubao-Seed-1.6 and o4, reveal a profound ``Reasoning Illusion''. We observe that high surface-level accuracy conceals a catastrophic failure in the models' ability to produce valid visual aids or to reason from them. Our findings expose a fundamental schism between visual perception and logical deduction in modern LMMs. We host an evaluation platform at CodaBench for testing publicly. Homepage: https://nlp2ct.github.io/VisAidMathHomepage/ Evaluation: https://www.codabench.org/competitions/7634/

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

arXiv:2508.01249v3 Announce Type: replace-cross 
Abstract: Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However, their dynamic and non-transparent behavior introduces critical security risks, particularly in the presence of prompt injection attacks. In this work, we propose a novel insight that treats the agent runtime traces as structured programs with analyzable semantics. Thus, we present AgentArmor, a program analysis framework that converts agent traces into graph intermediate representation-based structured program dependency representations (e.g., CFG, DFG, and PDG) and enforces security policies via a type system. AgentArmor consists of three key components: (1) a graph constructor that reconstructs the agent's runtime traces as graph-based intermediate representations with control and data flow described within; (2) a property registry that attaches security-relevant metadata of interacted tools \& data, and (3) a type system that performs static inference and checking over the intermediate representation. By representing agent behavior as structured programs, AgentArmor enables program analysis for sensitive data flow, trust boundaries, and policy violations. We evaluate AgentArmor on the AgentDojo benchmark, the results show that AgentArmor can reduce the ASR to 3\%, with the utility drop only 1\%.

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

arXiv:2511.14691v1 Announce Type: cross 
Abstract: Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. While brain attention emerges from neural circuits, Transformer attention relies on dot-product similarity to weight elements in the input sequence. Neuromorphic computing, especially spiking neural networks (SNNs), offers a brain-inspired path to energy-efficient intelligence. Despite recent work on attention-based spiking Transformers, the core attention layer remains non-neuromorphic. Current spiking attention (i) relies on dot-product or element-wise similarity suited to floating-point operations, not event-driven spikes; (ii) keeps attention matrices that suffer from the von Neumann bottleneck, limiting in-memory computing; and (iii) still diverges from brain-like computation. To address these issues, we propose the Spiking STDP Transformer (S$^{2}$TDPT), a neuromorphic Transformer that implements self-attention through spike-timing-dependent plasticity (STDP), embedding query--key correlations in synaptic weights. STDP, a core mechanism of memory and learning in the brain and widely studied in neuromorphic devices, naturally enables in-memory computing and supports non-von Neumann hardware. On CIFAR-10 and CIFAR-100, our model achieves 94.35\% and 78.08\% accuracy with only four timesteps and 0.49 mJ on CIFAR-100, an 88.47\% energy reduction compared to a standard ANN Transformer. Grad-CAM shows that the model attends to semantically relevant regions, enhancing interpretability. Overall, S$^{2}$TDPT illustrates how biologically inspired attention can yield energy-efficient, hardware-friendly, and explainable neuromorphic models.

Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer

arXiv:2511.08955v2 Announce Type: replace-cross 
Abstract: Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science.

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction

<h1>
  
  
  Sector HQ Weekly Digest - November 17, 2025
</h1>

<p>Who's shipping vs who's just talking? Here's this week's AI industry intelligence.</p>

<h2>
  
  
  🏆 Top 10 Companies
</h2>

<ol>
<li>
<strong>OpenAI</strong> - Score: 442385.7 | 343 events this week</li>
<li>
<strong>Anthropic</strong> - Score: 266113.1 | 51 events this week</li>
<li>
<strong>Amazon</strong> - Score: 135242.8 | 22 events this week</li>
<li>
<strong>Nvidia</strong> - Score: 128360.7 | 161 events this week</li>
<li>
<strong>Google</strong> - Score: 111135.8 | 125 events this week</li>
<li>
<strong>Microsoft</strong> - Score: 95559.7 | 99 events this week</li>
<li>
<strong>Meta</strong> - Score: 78015.6 | 61 events this week</li>
<li>
<strong>Apple</strong> - Score: 64936.3 | 94 events this week</li>
<li>
<strong>Tesla</strong> - Score: 41242.1 | 12 events this week</li>
<li>
<strong>AMD</strong> - Score: 40235.9 | 43 events this week</li>
</ol>

<h2>
  
  
  📈 Biggest Movers
</h2>

<ul>
<li>↑ <strong>Sony</strong> jumped 277 positions to #58</li>
<li>↑ <strong>Stability AI</strong> jumped 183 positions to #78</li>
<li>↑ <strong>Bytedance</strong> jumped 143 positions to #68</li>
<li>↑ <strong>Scale AI</strong> jumped 122 positions to #66</li>
<li>↑ <strong>Palantir</strong> jumped 107 positions to #17</li>
</ul>

<h2>
  
  
  🚨 Hype Gap Alerts
</h2>

<p><em>No high hype alerts this week</em></p>

<h2>
  
  
  📊 Key Insights
</h2>

<ul>
<li>Total companies tracked: 100</li>
<li>Total events this week: 1317</li>
<li>Average activity per company: 13.2 events</li>
</ul>

<h2>
  
  
  💡 What This Means
</h2>

<p>The AI industry continues to evolve rapidly. Companies that ship consistently rise in our rankings, while those focused on hype alone get flagged by our Hype Gap detector.</p>

<p><strong>Methodology</strong>: Our leaderboard tracks real product releases, funding events, partnerships, and market traction - not just PR and social media buzz.</p>




<p><em>Want real-time updates? Check out the live leaderboard at <a href="https://sectorhq.co" rel="noopener noreferrer">sectorhq.co</a></em></p>

<p><em>Track specific companies and get instant alerts when they move in the rankings.</em></p>

<h2>
  
  
  Tags
</h2>

<h1>
  
  
  AI #ArtificialIntelligence #MachineLearning #TechIndustry #Startups #AILeaderboard
</h1>

تسليط الضوء على أحدث التطورات في صناعة الذكاء الاصطناعي في نشرة قطاع HQ الأسبوعية بتاريخ 17 نوفمبر 2025، مع التركيز على أداء الشركات الرائدة. تتصدر OpenAI القائمة برصيد 442385.7 و343 حدثًا، تليها Anthropic وAmazon. كما يشير التقرير إلى تحركات كبيرة، حيث قفزت Sony 277 مركزًا في الترتيب، مما يعكس الطبيعة الديناميكية للقطاع.

El Digest semanal de Sector HQ del 17 de noviembre de 2025 destaca los últimos desarrollos en la industria de la IA, centrándose en el rendimiento de las principales empresas. OpenAI lidera con una puntuación de 442385.7 y 343 eventos, seguido de Anthropic y Amazon. El informe también señala movimientos significativos, con Sony saltando 277 posiciones en el ranking, reflejando la naturaleza dinámica del sector de la IA.

Le Digest hebdomadaire du Secteur HQ du 17 novembre 2025 met en lumière les derniers développements de l'industrie de l'IA, en se concentrant sur la performance des principales entreprises. OpenAI est en tête avec un score de 442385,7 et 343 événements, suivi d'Anthropic et d'Amazon. Le rapport note également des mouvements significatifs, Sony ayant grimpé de 277 positions dans le classement, reflétant la nature dynamique du secteur de l'IA.

The Sector HQ Weekly Digest for November 17, 2025, highlights the latest developments in the AI industry, focusing on the performance of top companies. OpenAI leads with a score of 442385.7 and 343 events, followed by Anthropic and Amazon. The report also notes significant movements, with Sony jumping 277 positions in the rankings, reflecting the dynamic nature of the AI sector.

Sector HQ Weekly Digest - November 17, 2025

Faster and safer deliveries are on the way.

قد يستخدم سائقو أمازون قريبًا نظارات ذكية للتوصيل، مما يعزز كفاءة وأمان عملية التوصيل. من المتوقع أن تعمل هذه الابتكارات على تحسين العمليات وزيادة رضا العملاء. تمثل دمج التكنولوجيا الذكية في اللوجستيات تقدمًا كبيرًا في قطاع التجارة الإلكترونية.

Los conductores de Amazon podrían pronto utilizar gafas inteligentes para las entregas, mejorando la eficiencia y seguridad del proceso de entrega. Esta innovación se espera que optimice las operaciones y mejore la satisfacción del cliente. La integración de tecnología inteligente en la logística representa un avance significativo en el sector del comercio electrónico.

Les livreurs d'Amazon pourraient bientôt utiliser des lunettes intelligentes pour les livraisons, améliorant ainsi l'efficacité et la sécurité du processus de livraison. Cette innovation devrait rationaliser les opérations et améliorer la satisfaction client. L'intégration de la technologie intelligente dans la logistique représente une avancée significative dans le secteur du commerce électronique.

Amazon drivers may soon utilize smart glasses for deliveries, enhancing the efficiency and safety of the delivery process. This innovation is expected to streamline operations and improve customer satisfaction. The integration of smart technology into logistics represents a significant advancement in the e-commerce sector.

Your Amazon driver may soon deliver with these smart glasses on - why that's good news

سيشهد يوم الجمعة السوداء لعام 2025 مبيعات كبيرة في التكنولوجيا من شركات كبرى مثل أمازون وآبل وليغو ونينجا. من المتوقع أن يقدم هذا الحدث، الذي سيقام في 15 نوفمبر 2025، بعضًا من أفضل العروض في مجال التكنولوجيا، مما يجذب المستهلكين الذين يبحثون عن خصومات على المنتجات الشهيرة. مع اقتراب موسم التسوق، تستعد هذه الشركات لعرض أحدث عروضها وترويجاتها لتعظيم المبيعات خلال هذه الفترة ذات الحركة العالية.

El Black Friday de 2025 contará con importantes ventas de tecnología de empresas como Amazon, Apple, Lego y Ninja. Este evento, que se llevará a cabo el 15 de noviembre de 2025, se espera que ofrezca algunas de las mejores ofertas en tecnología, atrayendo a consumidores que buscan descuentos en productos populares. A medida que se acerca la temporada de compras, estas empresas se están preparando para mostrar sus últimas ofertas y promociones para maximizar las ventas durante este período de alto tráfico comercial.

Le Black Friday 2025 sera marqué par d'importantes ventes de technologie de la part de grandes entreprises telles qu'Amazon, Apple, Lego et Ninja. L'événement, qui se déroulera le 15 novembre 2025, devrait offrir certaines des meilleures offres en matière de technologie, attirant les consommateurs à la recherche de réductions sur des produits populaires. À l'approche de la saison des achats, ces entreprises se préparent à présenter leurs dernières offres et promotions pour maximiser les ventes pendant cette période de forte affluence commerciale.

Black Friday 2025 is set to feature significant tech sales from major companies including Amazon, Apple, Lego, and Ninja. The event, occurring on November 15, 2025, is anticipated to offer some of the best deals in technology, attracting consumers looking for discounts on popular products. As the shopping season approaches, these companies are preparing to showcase their latest offerings and promotions to maximize sales during this high-traffic retail period.

Black Friday deals for 2025: The best tech sales from Amazon, Apple, Lego, Ninja and more

Constellation Energy will use the loan to refurbish a reactor it idled in 2019. Microsoft has agreed to buy the power once the facility reopens in 2028.

Trump DOE gives Microsoft partner $1B loan to restart Three Mile Island reactor

Taking a page from Big Tech, legacy industries, and their U.S. peers, European tech companies are increasingly learning to lobby for themselves.

European tech gets political

The permit is the final regulatory step to launch a robotaxi service in Arizona, a state that has become a hub of autonomous vehicle technology testing and development.

Amazon satellite network gets a rebrand — and drops its affordability pitch

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