This is today&#8217;s edition of The Download, our weekday newsletter that provides a daily dose of what&#8217;s going on in the world of technology. Quantum physicists have shrunk and “de-censored” DeepSeek R1 The news: A group of quantum physicists at Spanish firm Multiverse Computing claims to have created a version of the powerful reasoning AI model DeepSeek&#8230;

طور علماء الفيزياء الكمومية في شركة Multiverse Computing الإسبانية نسخة من نموذج الذكاء الاصطناعي DeepSeek تم 'إزالة الرقابة' عنها وتحسين أدائها. تعتبر هذه الخطوة مهمة في مجال الذكاء الاصطناعي، حيث تعزز من قدرات النموذج على التفكير. تسلط هذه الأخبار الضوء على الابتكارات المستمرة في تكنولوجيا الذكاء الاصطناعي وإمكانية تحسين التطبيقات في مختلف القطاعات.

Físicos cuánticos de Multiverse Computing en España han desarrollado una versión del modelo de IA DeepSeek que ha sido 'desensibilizada' y optimizada para el rendimiento. Este avance es significativo en el campo de la inteligencia artificial, ya que mejora las capacidades de razonamiento del modelo. La noticia destaca las innovaciones continuas en la tecnología de IA y el potencial para aplicaciones mejoradas en varios sectores.

Des physiciens quantiques de Multiverse Computing en Espagne ont développé une version du modèle d'IA DeepSeek qui a été 'dé-censurée' et optimisée pour la performance. Cette avancée est significative dans le domaine de l'intelligence artificielle, car elle améliore les capacités de raisonnement du modèle. Cette nouvelle met en lumière les innovations continues dans la technologie de l'IA et le potentiel d'applications améliorées dans divers secteurs.

Quantum physicists at Multiverse Computing in Spain have developed a version of the AI model DeepSeek that has been 'de-censored' and optimized for performance. This advancement is significant in the field of artificial intelligence, as it enhances the model's reasoning capabilities. The news highlights ongoing innovations in AI technology and the potential for improved applications in various sectors.

The Download: de-censoring DeepSeek, and Gemini 3

The story of the Ghost in the Shell’s main villain the Puppet Master hinted at a future where governments use hackers for espionage, at a time when most of the world had never connected to the internet.

الأنمي الكلاسيكي 'Ghost in the Shell' يقدم شخصية Puppet Master، التي تتنبأ بمستقبل تستخدم فيه الحكومات القراصنة للتجسس. ظهرت هذه التنبؤات في وقت كانت فيه غالبية سكان العالم لم تتصل بعد بالإنترنت، مما يبرز رؤية العرض لمشكلات الأمن السيبراني.

El clásico anime 'Ghost in the Shell' presenta al Puppet Master, un personaje que anticipa un futuro en el que los gobiernos utilizan hackers para el espionaje. Esta predicción surgió en un momento en que la mayoría de la población mundial aún no estaba conectada a Internet, destacando la previsión del programa sobre los problemas de ciberseguridad.

L'anime classique 'Ghost in the Shell' présente le Puppet Master, un personnage qui préfigure un avenir où les gouvernements utilisent des hackers pour l'espionnage. Cette prédiction est survenue à une époque où la majorité de la population mondiale n'était pas encore connectée à Internet, soulignant la prévoyance de l'émission concernant les problèmes de cybersécurité.

The classic anime 'Ghost in the Shell' features the Puppet Master, a character that foreshadows a future where governments utilize hackers for espionage. This prediction emerged at a time when the majority of the global population had yet to connect to the internet, highlighting the show's foresight regarding cybersecurity issues.

How the classic anime ‘Ghost in the Shell’ predicted the future of cybersecurity 30 years ago

<p>Text-to-image diffusion models have become the workhorses of generative imaging. They can paint photorealistic scenes, mimic art styles, and blend concepts in ways that were science fiction a few years ago. Yet they stumble embarrassingly on a skill that even small children master: basic spatial reasoning.</p>

<p>Ask a state-of-the-art model for “a dog to the right of a teddy bear” and you often get:</p>

<ul>
<li>The dog on the left</li>
<li>One of the objects missing</li>
<li>Or a bizarre hybrid where dog and teddy are fused into a single creature</li>
</ul>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" alt=" " width="800" height="532"></a></p>

<p>These failures become more severe for unusual compositions like “a giraffe above an airplane”. Traditional fixes range from expensive fine-tuning to brittle, hand-written loss functions at inference time—but both options come with significant downsides.</p>

<p>NVIDIA’s Learn-to-Steer framework (accepted to WACV 2026) proposes a different path: instead of hard-coding spatial rules or retraining the entire model, it learns a data-driven objective that can “steer” diffusion at inference time. The method reads the model’s own cross-attention maps, trains a lightweight classifier to detect spatial relations, and then uses that classifier’s gradient as a learned loss to nudge the generation towards layouts that match the prompt.</p>

<p>In this blog, we’ll unpack:</p>

<ul>
<li>What makes spatial reasoning so fragile in current diffusion models</li>
<li>How Learn-to-Steer learns spatial constraints from the model itself</li>
<li>How it steers images during generation without changing model weights</li>
<li>The top gains on spatial benchmarks like GenEval and T2I-CompBench</li>
<li>The trade-offs in compute cost and generality, and what this implies for future generative systems</li>
</ul>

<h1>
  
  
  Why Spatial Reasoning Fails in Text-to-Image Diffusion
</h1>

<h2>
  
  
  What Makes Spatial Relations So Difficult for Diffusion Models?
</h2>

<p>Modern diffusion models (e.g., Stable Diffusion, Flux) are excellent at what should appear in an image—objects, styles, textures—but much less reliable at where those objects should be.</p>

<p>Several factors contribute:</p>

<h3>
  
  
  Weak supervision of spatial language
</h3>

<ul>
<li>Training data rarely comes with precise annotations like “object A is left of object B”.
</li>
<li>Captions often describe content loosely, so phrases like “on top of” or “to the right of” are under-specified.</li>
</ul>

<h3>
  
  
  Entangled visual concepts
</h3>

<ul>
<li>When two objects frequently co-occur, models may treat them as a single visual blob.</li>
<li>This leads to object fusion, where a “cat on a bookshelf” becomes a cat-bookshelf chimera.</li>
</ul>

<h3>
  
  
  Benchmark saturation without spatial coverage
</h3>

<ul>
<li>Many standard text-to-image benchmarks emphasize realism and style, not relational accuracy.</li>
<li>Models can score highly while still being spatially confused.</li>
</ul>

<p>Empirical studies confirm three recurring failure modes on spatial benchmarks:</p>

<ul>
<li>Incorrect placement: Objects appear in the wrong relative position.</li>
<li>Missing entities: One or more requested objects never appear.</li>
<li>Merged entities: Two objects get mashed into a single, incoherent form.</li>
</ul>

<p>The model “knows” the objects you asked for, but it doesn’t reliably understand where to place them.</p>

<h1>
  
  
  Why Fine-Tuning and Handcrafted Losses Are Not Enough
</h1>

<p>Two broad strategies have tried to patch this gap:</p>

<h2>
  
  
  Fine-tuning for spatial awareness
</h2>

<ul>
<li>Retrain the diffusion model on datasets with explicit layouts or spatial annotations.</li>
<li>Methods like COMPASS show that this can significantly improve spatial accuracy.</li>
<li>But this comes at a cost: expensive retraining, sensitivity to dataset bias, and often regressions in other capabilities such as color fidelity or counting.</li>
</ul>

<h2>
  
  
  Handcrafted test-time losses
</h2>

<ul>
<li>At inference, inject extra loss terms that penalize spatial errors (e.g., overlapping activation maps, incorrect ordering).</li>
<li>These losses must be manually designed to approximate relations like “left of” or “above”.</li>
<li>In practice, these heuristics are fragile, often over-fitting simple cases and failing on more complex layouts.</li>
</ul>

<p>In short, we’ve lacked a solution that is:</p>

<ul>
<li>Data-driven rather than rule-based</li>
<li>Plug-and-play at inference time (no full retraining)</li>
<li>Targeted enough to improve spatial reasoning without damaging other strengths</li>
</ul>

<p>This is where Learn-to-Steer enters.</p>

<h1>
  
  
  How Learn-to-Steer Works: Data-Driven Steering at Inference
</h1>

<h2>
  
  
  How Cross-Attention Maps Provide a Spatial Signal
</h2>

<p>During diffusion, at each denoising step, the model computes cross-attention maps that connect text tokens to image regions. For a prompt like “a dog to the right of a teddy bear”, you can think of:</p>

<ul>
<li>One set of attention maps for “dog”</li>
<li>Another set for “teddy bear”</li>
<li>Additional context around words like “right” or “of”</li>
</ul>

<p>These maps form a rich, high-dimensional signal describing where in the image the model currently believes each word should manifest. Prior work has used cross-attention to locate objects or edit images; Learn-to-Steer goes further by treating them as a feature space in which spatial relations can be learned.</p>

<h2>
  
  
  How a Relation Classifier Becomes a Learned Loss
</h2>

<p>The core idea of Learn-to-Steer is to train a small relation classifier that takes cross-attention maps for two objects and predicts the spatial relation between them (left-of, right-of, above, below, etc.).</p>

<p>The pipeline looks like this:</p>

<h3>
  
  
  Collect supervision
</h3>

<ul>
<li>Use images where the true relation between object A and object B is known (from datasets like GQA and synthetic layouts).</li>
<li>For each image, invert it through the diffusion model with a descriptive prompt to recover cross-attention maps for the relevant tokens.</li>
</ul>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" alt=" " width="800" height="446"></a></p>

<h3>
  
  
  Train a classifier on attention patterns
</h3>

<ul>
<li>Input: attention maps for object A and object B.</li>
<li>Output: predicted relation (e.g., “A is left of B”).</li>
</ul>

<p>Naively, however, this leads to a subtle but serious issue: relation leakage.</p>

<h2>
  
  
  How Dual Inversion Solves the “Relation Leakage” Problem
</h2>

<p>If you always invert images with a correct prompt (e.g., “a dog to the left of a cat”), hints about the word “left” can leak into the attention patterns. A naïve classifier might then “cheat” by reading out linguistic artefacts instead of learning genuine visual geometry.</p>

<p>To prevent this, Learn-to-Steer uses a dual inversion strategy:</p>

<ul>
<li>For each image with a true relation (say, dog left of cat), create two prompts:

<ul>
<li>A positive prompt with the correct relation (“dog to the left of a cat”).</li>
<li>A negative prompt with an incorrect relation (“dog above a cat”).</li>
</ul>


</li>

<li>Run inversion with both prompts, obtaining two sets of attention maps.</li>

<li>Label both sets with the true relation (left-of), because that is what the image actually depicts.</li>

</ul>

<p>The classifier sees pairs of attention maps that share the same underlying geometry but differ in the relation words used in the prompt. To succeed, it must ignore the unreliable linguistic cue and zero in on the geometric evidence in the attention patterns. This breaks the leakage shortcut and yields a classifier that actually understands “left-of” in terms of where things appear in the model’s internal vision.</p>

<p>To improve robustness, NVIDIA combines:</p>

<ul>
<li>Real images (complex, natural scenes)</li>
<li>Synthetic images (simpler, cleaner attention patterns akin to generation scenarios)</li>
</ul>

<h1>
  
  
  How Learn-to-Steer Guides Images During Generation
</h1>

<h2>
  
  
  Step-by-Step: From Prompt to Steered Latent
</h2>

<p>Once the relation classifier is trained, Learn-to-Steer uses it at inference time as a learned objective:</p>

<h3>
  
  
  Parse the spatial prompt
</h3>

<ul>
<li>Extract subject, relation, and object from the text (e.g., subject = dog, relation = right-of, object = teddy bear).</li>
</ul>

<h3>
  
  
  Run diffusion as usual—but with checkpoints
</h3>

<ul>
<li>As the model denoises latent noise into an image, periodically extract cross-attention maps for the subject and object tokens.</li>
</ul>

<h3>
  
  
  Evaluate spatial correctness
</h3>

<ul>
<li>Feed these maps into the relation classifier, which outputs a probability distribution over relations.</li>
<li>Compare this distribution to the desired relation from the prompt, and compute a loss (e.g., cross-entropy).</li>
</ul>

<h3>
  
  
  Backpropagate into the latent
</h3>

<ul>
<li>Compute the gradient of this loss with respect to the latent representation at that timestep.</li>
<li>Nudge the latent in the direction that increases the classifier’s confidence in the correct relation.</li>
</ul>

<h3>
  
  
  Continue the diffusion process
</h3>

<ul>
<li>Let the denoising proceed from the adjusted latent.</li>
<li>Repeat this steering a number of times (often during the earlier half of the diffusion steps).</li>
</ul>

<h2>
  
  
  Support for Multiple Architectures and Relations
</h2>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" alt=" " width="800" height="477"></a></p>

<p>A key advantage of Learn-to-Steer is that it’s architecture-agnostic:</p>

<ul>
<li>It has been demonstrated on both UNet-based models (like Stable Diffusion 1.4/2.1) and MMDiT-style models (like Flux).</li>
<li>The only requirement is access to a text-image alignment signal (cross-attention or similar).</li>
</ul>

<p>It can also handle prompts with multiple constraints, such as:</p>

<p>“A frog above a sneaker below a teapot.”</p>

<p>Here, Learn-to-Steer alternates attention between relations:</p>

<ul>
<li>At one timestep, optimize the frog–sneaker relation.</li>
<li>At another, optimize the sneaker–teapot relation.</li>
</ul>

يهدف Learn-to-Steer من NVIDIA إلى معالجة قيد كبير في نماذج الانتشار من النص إلى الصورة، التي تعاني من ضعف في التفكير المكاني الأساسي. يمكن لهذه النماذج إنشاء صور فوتوغرافية واقعية، لكنها غالبًا ما تضع الأشياء في غير موضعها، مثل وضع كلب على اليسار بدلاً من اليمين بجانب دمية دب. تهدف هذه الخطوة إلى تحسين دقة الصور المولدة من خلال تعزيز الفهم المكاني.

El Learn-to-Steer de NVIDIA busca abordar una limitación significativa en los modelos de difusión de texto a imagen, que luchan con el razonamiento espacial básico. Estos modelos pueden crear imágenes fotorealistas, pero a menudo colocan mal los objetos en relación entre sí, como poner un perro a la izquierda de un oso de peluche en lugar de a la derecha. Este avance tiene como objetivo mejorar la precisión de las imágenes generadas al mejorar la comprensión espacial.

Le Learn-to-Steer de NVIDIA vise à résoudre une limitation importante des modèles de diffusion texte-image, qui ont du mal avec le raisonnement spatial de base. Ces modèles peuvent créer des images photoréalistes mais placent souvent mal les objets les uns par rapport aux autres, comme mettre un chien à gauche d'un ours en peluche au lieu de la droite. Cette avancée vise à améliorer l'exactitude des images générées en renforçant la compréhension spatiale.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

What Is Learn-to-Steer? NVIDIA’s 2025 Spatial Fix for Text-to-Image Diffusion

<p>The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chips</p><p>Nvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates.<strong> </strong>All eyes were on Nvidia, the bellwether for the AI industry and the most valuable publicly traded company in the world, as analysts and investors hoped the chipmaker’s third-quarter earnings would assuage concerns about whether the high-flying valuations of AI firms have peaked.</p><p>“Blackwell sales are off the charts, and cloud GPUs are sold out,” said Jensen Huang, founder and CEO of Nvidia in a press release. “Compute demand keeps accelerating and compounding across training and inference – each growing exponentially. We’ve entered the virtuous cycle of AI. The AI ecosystem is scaling fast – with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once.”</p> <a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report">Continue reading...</a>

تجاوزت شركة إنفيديا توقعات وول ستريت مع نتائجها للربع الثالث، مما أظهر طلبًا قويًا على شرائح الذكاء الاصطناعي الخاصة بها. ارتفعت أسهم الشركة في التداول بعد السوق، مما يعكس ثقة المستثمرين وسط مخاوف بشأن تقييم سوق الذكاء الاصطناعي. وأبرز الرئيس التنفيذي جينسن هوانغ مبيعات قياسية ونظامًا بيئيًا سريع التوسع في مجال الذكاء الاصطناعي، مما يشير إلى نظرة إيجابية لمستقبل الشركة.

Nvidia superó las expectativas de Wall Street con sus ganancias del tercer trimestre, mostrando una fuerte demanda por sus chips de IA. Las acciones de la compañía aumentaron en el comercio posterior al cierre, reflejando la confianza de los inversores en medio de preocupaciones sobre la valoración del mercado de IA. El CEO Jensen Huang destacó las ventas récord y un ecosistema de IA en rápida expansión, indicando una perspectiva positiva para el futuro de la empresa.

Nvidia a dépassé les attentes de Wall Street avec ses résultats du troisième trimestre, montrant une forte demande pour ses puces d'IA. Les actions de l'entreprise ont augmenté lors des échanges après la clôture, reflétant la confiance des investisseurs face aux préoccupations concernant la valorisation du marché de l'IA. Le PDG Jensen Huang a souligné des ventes record et un écosystème IA en pleine expansion, indiquant une perspective positive pour l'avenir de l'entreprise.

Nvidia exceeded Wall Street expectations with its third-quarter earnings, showcasing strong demand for its AI chips. The company's shares rose in after-market trading, reflecting investor confidence amid concerns about the AI market's valuation. CEO Jensen Huang highlighted record sales and a rapidly expanding AI ecosystem, indicating a positive outlook for the company's future.

‘AI is going everywhere, doing everything:’ Nvidia beats Wall Street estimates amid market selloff and AI bubble fears

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

<p>Swift 6.3 is bringing significant enhancements to Embedded Swift, the subset of Swift designed for resource-constrained environments like microcontrollers. Here's what's new:</p>

<h2>
  
  
  Key Improvements
</h2>

<h3>
  
  
  Libraries &amp; Standard Library
</h3>

<ul>
<li>
<strong>Floating-point printing</strong>: The <code>description</code> and <code>debugDescription</code> properties now work for Float, Double, and other floating-point types with a new all-Swift implementation</li>
<li>
<strong>Better diagnostics</strong>: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
<strong>Swift MMIO 0.1.x</strong>: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
  
  
  C Interoperability
</h3>

<ul>
<li>
<strong><code>@c</code> attribute</strong>: Define C-compatible functions and enums (from SE-0495)
</li>
</ul>

<div class="highlight js-code-highlight">
<pre class="highlight swift"><code><span class="kd">@c</span><span class="p">(</span><span class="kt">MyLib_initialize</span><span class="p">)</span>
<span class="kd">public</span> <span class="kd">func</span> <span class="nf">initialize</span><span class="p">()</span> <span class="p">{</span> <span class="o">...</span> <span class="p">}</span>
</code></pre>

</div>



<ul>
<li>
<strong>Improved type matching</strong>: Better tolerance for mismatching C signatures, eliminating cryptic deserialization errors</li>
</ul>

<h3>
  
  
  Debugging
</h3>

<ul>
<li>
<strong>Enhanced LLDB support</strong>: Better value printing for Embedded Swift types</li>
<li>
<strong>Core dump inspection</strong>: Dictionary, Array, and other common types now inspectable without a live process</li>
<li>
<strong>ARMv7m exception unwinding</strong>: Complete backtraces through exception frames</li>
</ul>

<h3>
  
  
  Linking &amp; Compilation
</h3>

<ul>
<li>
<strong><code>@section</code> and <code>@used</code> attributes</strong>: Control where globals are emitted and ensure symbols aren't stripped (SE-0492)</li>
<li>
<strong>Weak symbol definitions</strong>: Fixes duplicate symbol errors in diamond dependencies</li>
<li>
<strong><code>@export</code> attribute</strong>: Better control over function visibility (SE-0497)</li>
</ul>




<p><em>Want to dive deeper? Read the <a href="https://www.swift.org/blog/embedded-swift-improvements-coming-in-swift-6.3/" rel="noopener noreferrer">full announcement</a> on Swift.org</em></p>

تقدم Swift 6.3 تحسينات كبيرة على Embedded Swift، مما يعزز وظيفته في البيئات ذات الموارد المحدودة مثل المتحكمات الدقيقة. تشمل التحسينات الرئيسية قدرات جديدة لطباعة الأعداد العشرية، وتشخيصات أفضل مع مجموعة EmbeddedRestrictions، وإدخال Swift MMIO 0.1.x لتوليد الشيفرة وتصحيح الأخطاء.

Swift 6.3 presenta mejoras significativas en Embedded Swift, aumentando su funcionalidad para entornos con recursos limitados como microcontroladores. Las mejoras clave incluyen nuevas capacidades de impresión de números de punto flotante, mejores diagnósticos con el grupo EmbeddedRestrictions y la introducción de Swift MMIO 0.1.x para la generación de código y la depuración.

Swift 6.3 apporte des améliorations significatives à Embedded Swift, renforçant sa fonctionnalité pour les environnements à ressources limitées comme les microcontrôleurs. Les principales améliorations comprennent de nouvelles capacités d'impression de nombres à virgule flottante, de meilleurs diagnostics avec le groupe EmbeddedRestrictions, et l'introduction de Swift MMIO 0.1.x pour la génération de code et le débogage.

Swift 6.3 introduces significant upgrades to Embedded Swift, enhancing its functionality for resource-constrained environments like microcontrollers. Key improvements include new floating-point printing capabilities, better diagnostics with the EmbeddedRestrictions group, and the introduction of Swift MMIO 0.1.x for code generation and debugging.

Embedded Swift Gets Major Upgrades in Swift 6.3

<a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-252.jpg" width="800" height="560" style="padding: 15px 0" title="Judge dismisses lawsuit twice due to alleged deepfake video testimony" /></a><br />A California housing dispute is getting media attention over allegations that lawyers presented a deepfake video as witness testimony. NBC News reports that Judge Victoria Kolakowski became suspicious after the supposed witness showed signs that something was not right, including a monotone voice, fuzzy facial features, and repeated facial expressions....<br /><br /><a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a><br /><br />

تجذب نزاع سكني في كاليفورنيا الانتباه الإعلامي بعد ظهور مزاعم بأن المحامين قدموا فيديو مزيف كدليل شهود. أعربت القاضية فيكتوريا كولاكوفسكي عن شكوكها بشأن الفيديو، مشيرة إلى صوت الشاهد الأحادي، وملامح الوجه غير الواضحة، وتكرار التعبيرات. أدى ذلك إلى رفض الدعوى القضائية مرتين.

Una disputa de vivienda en California ha llamado la atención de los medios tras las alegaciones de que los abogados presentaron un video deepfake como testimonio. La jueza Victoria Kolakowski expresó su escepticismo sobre el video, señalando la voz monótona del testigo, rasgos faciales borrosos y expresiones repetitivas. Esto llevó al desestimado de la demanda en dos ocasiones.

Un litige immobilier en Californie suscite l'attention des médias après des allégations selon lesquelles des avocats auraient présenté une vidéo deepfake comme témoignage. La juge Victoria Kolakowski a exprimé des doutes sur la vidéo, notant la voix monotone du témoin, des traits faciaux flous et des expressions répétitives. Cela a conduit à l'annulation de la poursuite à deux reprises.

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Judge dismisses lawsuit twice due to alleged deepfake video testimony

A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators.&#160; The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller&#8230;

طور فريق من الفيزيائيين الكموميين نسخة جديدة من نموذج الذكاء الاصطناعي DeepSeek R1، تُدعى DeepSeek R1 Slim، والتي هي أصغر بنسبة 55% وتزيل الرقابة المفروضة من قبل منشئيها الصينيين الأصليين. تم تحقيق هذا التقدم من قبل شركة Multiverse Computing الإسبانية المتخصصة في تقنيات الذكاء الاصطناعي المستوحاة من الفيزياء الكمومية.

Un equipo de físicos cuánticos ha desarrollado una nueva versión del modelo de IA DeepSeek R1, llamada DeepSeek R1 Slim, que es un 55% más pequeña y elimina la censura impuesta por sus creadores chinos originales. Este avance fue realizado por Multiverse Computing, una empresa española especializada en técnicas de IA inspiradas en la física cuántica.

Une équipe de physiciens quantiques a développé une nouvelle version du modèle d'IA DeepSeek R1, appelée DeepSeek R1 Slim, qui est 55 % plus petite et supprime la censure imposée par ses créateurs chinois d'origine. Cette avancée a été réalisée par Multiverse Computing, une entreprise espagnole spécialisée dans les techniques d'IA inspirées par la physique quantique.

A team of quantum physicists has developed a new version of the AI model DeepSeek R1, named DeepSeek R1 Slim, which is 55% smaller and removes the censorship imposed by its original Chinese creators. This advancement was made by Multiverse Computing, a Spanish company specializing in quantum-inspired AI techniques.

Quantum physicists have shrunk and “de-censored” DeepSeek R1

arXiv:2511.13764v1 Announce Type: new 
Abstract: The rapidly evolving landscape of AI and machine learning workloads has widened the gap between high-level domain operations and efficient hardware utilization. Achieving near-peak performance still demands deep hardware expertise-experts either handcraft target-specific kernels (e.g., DeepSeek) or rely on specialized libraries (e.g., CUTLASS)-both of which add complexity and limit scalability for most ML practitioners.
  This paper introduces a compilation scheme that automatically generates scalable, high-performance microkernels by leveraging the MLIR dialects to bridge domain-level operations and processor capabilities. Our approach removes dependence on low-level libraries by enabling the compiler to auto-generate near-optimal code directly. At its core is a mechanism for composing nanokernels from low-level IR constructs with near-optimal register utilization, forming efficient microkernels tailored to each target. We implement this technique in an MLIR-based compiler supporting both vector and tile based CPU instructions. Experiments show that the generated nanokernels are of production-quality, and competitive with state-of-the-art microkernel libraries.

يتناول المقال بعنوان 'تحرير المكتبة: أداء تنافسي لمصفوفة الضرب من خلال نانوكرنلز مؤلفة بواسطة المترجم' التحديات في تحسين أحمال العمل في الذكاء الاصطناعي وتعلم الآلة. يقدم مخطط تجميع يقوم بإنشاء نانوكرنلز قابلة للتوسع باستخدام لهجات MLIR، مما يقلل الاعتماد على المكتبات منخفضة المستوى. تهدف هذه الابتكارات إلى تحسين الأداء وتبسيط عملية التطوير لممارسي تعلم الآلة، مما يمكنهم من تحقيق أداء قريب من الذروة دون الحاجة إلى خبرة عميقة في الأجهزة.

El artículo titulado 'Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels' aborda los desafíos en la optimización de cargas de trabajo de IA y aprendizaje automático. Presenta un esquema de compilación que genera microkernels escalables utilizando dialectos de MLIR, reduciendo la dependencia de bibliotecas de bajo nivel. Esta innovación busca mejorar el rendimiento y simplificar el proceso de desarrollo para los practicantes de aprendizaje automático, permitiéndoles alcanzar un rendimiento cercano al máximo sin una profunda experiencia en hardware.

L'article intitulé 'Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels' aborde les défis d'optimisation des charges de travail en IA et en apprentissage automatique. Il présente un schéma de compilation qui génère des micro-noyaux évolutifs en utilisant des dialectes MLIR, réduisant ainsi la dépendance aux bibliothèques de bas niveau. Cette innovation vise à améliorer les performances et à simplifier le processus de développement pour les praticiens de l'apprentissage automatique, leur permettant d'atteindre des performances proches du maximum sans experti…

The paper titled 'Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels' addresses the challenges in optimizing AI and machine learning workloads. It presents a compilation scheme that generates scalable microkernels using MLIR dialects, reducing reliance on low-level libraries. This innovation aims to enhance performance and simplify the development process for machine learning practitioners, enabling them to achieve near-peak performance without deep hardware expertise.

Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels

Manufacturing is getting a major system upgrade. As AI amplifies existing technologies—like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)—it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization. Digital twins—physically accurate virtual representations of a piece of equipment, a production line, a process,&#8230;

تشهد صناعة التصنيع تحولًا كبيرًا حيث تعزز الذكاء الاصطناعي (AI) التقنيات الحالية مثل التوائم الرقمية، والحوسبة السحابية، والحوسبة الطرفية، وإنترنت الأشياء الصناعي (IIoT). يسمح هذا التحول لفرق عمليات المصانع بالانتقال من حل المشكلات التفاعلي إلى تحسين استباقي على مستوى النظام، مما يحسن الكفاءة والإنتاجية.

La manufactura está experimentando una transformación significativa a medida que la inteligencia artificial (IA) mejora tecnologías existentes como los gemelos digitales, la computación en la nube, la computación en el borde y el internet industrial de las cosas (IIoT). Este cambio permite a los equipos de operaciones de fábricas pasar de la resolución de problemas reactiva a la optimización proactiva en todo el sistema, mejorando la eficiencia y la productividad.

L'industrie manufacturière subit une transformation significative grâce à l'intelligence artificielle (IA), qui améliore des technologies existantes telles que les jumeaux numériques, le cloud computing, l'informatique en périphérie et l'internet industriel des objets (IIoT). Ce changement permet aux équipes d'exploitation des usines de passer d'une résolution de problèmes réactive à une optimisation proactive à l'échelle du système, améliorant ainsi l'efficacité et la productivité.

Manufacturing is undergoing a significant transformation as artificial intelligence (AI) enhances existing technologies such as digital twins, cloud computing, edge computing, and the industrial internet of things (IIoT). This shift allows factory operations teams to move from reactive problem-solving to proactive optimization across systems, improving efficiency and productivity.

The Download: de-censoring DeepSeek, and Gemini 3

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