<p class="feature-image"><a href="https://petapixel.com/2025/11/19/astronaut-captures-spectacular-aurora-lights-dancing-above-the-united-states/"><img width="1600" height="840" src="https://petapixel.com/assets/uploads/2025/11/ISS-Aurora-Lights.jpg" class="attachment-card-large size-card-large wp-post-image" alt="A view of Earth from space at night, showing bright city lights and a colorful aurora with green and pink hues in the atmosphere above the horizon." decoding="async" /></a></p><p>"I've never seen aurora from below, but up here, it's a frequent show," says astronaut Zena Cardman, who currently resides onboard the International Space Station.</p>
<p class="read-more">[<a href="https://petapixel.com/2025/11/19/astronaut-captures-spectacular-aurora-lights-dancing-above-the-united-states/">Read More</a>]</p>

التقطت رائدة الفضاء زينا كاردمان، التي تتواجد حاليًا على متن محطة الفضاء الدولية، صورًا مذهلة للأضواء الشمالية الراقصة فوق الولايات المتحدة. وأشارت كاردمان إلى أنه على الرغم من أنها لم تر الأضواء الشمالية من الأسفل، فإن مشاهدتها من الفضاء هي عرض متكرر.

La astronauta Zena Cardman, actualmente a bordo de la Estación Espacial Internacional, capturó impresionantes imágenes de luces aurorales danzando sobre los Estados Unidos. Cardman comentó que, aunque nunca había visto auroras desde abajo, presenciarlas desde el espacio es un espectáculo frecuente.

L'astronaute Zena Cardman, actuellement à bord de la Station spatiale internationale, a capturé des images époustouflantes des lumières aurorales dansant au-dessus des États-Unis. Cardman a noté que, bien qu'elle n'ait jamais vu d'aurores d'en bas, les observer depuis l'espace est une occurrence fréquente.

Astronaut Zena Cardman, currently aboard the International Space Station, captured stunning images of aurora lights dancing above the United States. Cardman noted that while she had never seen auroras from below, witnessing them from space is a frequent occurrence.

Astronaut Captures Spectacular Aurora Lights Dancing Above the United States

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

China has called on the United States to avoid interfering in Venezuela's internal affairs, responding to Washington's expanding military operations in the Caribbean and Eastern Pacific.

طالبت الصين الولايات المتحدة بعدم التدخل في الشؤون الداخلية لفنزويلا، ردًا على زيادة العمليات العسكرية الأمريكية في منطقة الكاريبي والمحيط الهادئ الشرقي. تعكس هذه التصريحات موقف الصين في الحفاظ على السلام في أمريكا اللاتينية.

China ha instado a Estados Unidos a evitar interferir en los asuntos internos de Venezuela, en respuesta a la creciente presencia militar estadounidense en el Caribe y el Pacífico oriental. Esta declaración refleja la posición de China sobre el mantenimiento de la paz en América Latina.

La Chine a exhorté les États-Unis à ne pas interférer dans les affaires internes du Venezuela, en réponse à l'augmentation de la présence militaire américaine dans les Caraïbes et le Pacifique oriental. Cette déclaration reflète la position de la Chine sur le maintien de la paix en Amérique latine.

China has urged the United States to refrain from interfering in Venezuela's internal affairs, in response to the U.S.'s increasing military presence in the Caribbean and Eastern Pacific. This statement reflects China's position on maintaining peace in Latin America.

China Claims Latin America Is as 'A Zone of Peace', Urges U.S. to Stop Interfering 'in Venezuela's Internal Affairs'

A new mega data center is slated to rise in a rural stretch of eastern Germany in what backers hope is a starting point for a European AI sector that can compete with the United States and China.

من المقرر بناء مركز بيانات ضخم جديد في شرق ألمانيا، مع الأمل في أن يكون نقطة انطلاق لقطاع الذكاء الاصطناعي الأوروبي الذي يمكنه المنافسة مع الولايات المتحدة والصين. هذه المبادرة هي جزء من استراتيجية ألمانيا الأوسع لتحقيق السيادة الرقمية.

Se planea construir un nuevo mega centro de datos en el este de Alemania, con la esperanza de establecer una base para un sector europeo de IA que pueda competir con Estados Unidos y China. Esta iniciativa forma parte de la estrategia más amplia de Alemania para lograr la soberanía digital.

Un nouveau méga centre de données doit être construit dans l'est de l'Allemagne, visant à établir une base pour un secteur européen de l'IA capable de rivaliser avec les États-Unis et la Chine. Cette initiative fait partie de la stratégie plus large de l'Allemagne pour atteindre la souveraineté numérique.

A new mega data center is set to be constructed in eastern Germany, aimed at establishing a foundation for a competitive European AI sector against the United States and China. This initiative is part of Germany's broader strategy to achieve digital sovereignty.

Germany hopes new data center can help bring 'digital sovereignty'

arXiv:2511.10459v2 Announce Type: replace 
Abstract: Large language models (LLMs) have been widely evaluated on macro-scale geographic tasks, such as global factual recall, event summarization, and regional reasoning. Yet, their ability to handle hyper-local knowledge remains poorly understood. This gap is increasingly consequential as real-world applications, from civic platforms to community journalism, demand AI systems that can reason about neighborhood-specific dynamics, cultural narratives, and local governance. Existing benchmarks fall short in capturing this complexity, often relying on coarse-grained data or isolated references. We present LocalBench, the first benchmark designed to systematically evaluate LLMs on county-level local knowledge across the United States. Grounded in the Localness Conceptual Framework, LocalBench includes 14,782 validated question-answer pairs across 526 U.S. counties in 49 states, integrating diverse sources such as Census statistics, local subreddit discourse, and regional news. It spans physical, cognitive, and relational dimensions of locality. Using LocalBench, we evaluate 13 state-of-the-art LLMs under both closed-book and web-augmented settings. Our findings reveal critical limitations: even the best-performing models reach only 56.8% accuracy on narrative-style questions and perform below 15.5% on numerical reasoning. Moreover, larger model size and web augmentation do not guarantee better performance, for example, search improves Gemini's accuracy by +13.6%, but reduces GPT-series performance by -11.4%. These results underscore the urgent need for language models that can support equitable, place-aware AI systems: capable of engaging with the diverse, fine-grained realities of local communities across geographic and cultural contexts.

تم تقديم LocalBench كمعيار جديد يهدف إلى تقييم نماذج اللغة الكبيرة (LLMs) على مستوى المعرفة المحلية والقدرة على التفكير على مستوى المقاطعات في الولايات المتحدة. يعالج هذا المعيار العيوب الموجودة في المعايير الحالية التي تركز بشكل أساسي على المهام الجغرافية على نطاق واسع. يتضمن LocalBench 14,782 زوجًا من الأسئلة والأجوبة المعتمدة عبر 526 مقاطعة أمريكية، ويجمع بين مصادر بيانات متنوعة مثل إحصاءات التعداد والمساهمات المحلية. هذه المبادرة ضرورية مع تزايد الحاجة إلى أنظمة الذكاء الاصطناعي لفهم الديناميات المحلية والحكم بشكل دقيق.

LocalBench es un nuevo estándar diseñado para evaluar modelos de lenguaje de gran tamaño (LLMs) en cuanto a su conocimiento local y razonamiento a nivel de condado en los Estados Unidos. Aborda las deficiencias de los estándares existentes que se centran principalmente en tareas geográficas a gran escala. LocalBench incluye 14,782 pares de preguntas y respuestas validadas en 526 condados de EE. UU., integrando diversas fuentes de datos como estadísticas del censo y aportes de la comunidad local. Esta iniciativa es crucial a medida que los sistemas de IA requieren una comprensión matizada de la…

LocalBench est un nouvel outil de référence conçu pour évaluer les modèles de langage de grande taille (LLMs) sur leurs connaissances locales et leur raisonnement au niveau des comtés aux États-Unis. Il répond aux insuffisances des références existantes qui se concentrent principalement sur des tâches géographiques à grande échelle. LocalBench comprend 14 782 paires de questions-réponses validées dans 526 comtés américains, intégrant diverses sources de données telles que les statistiques du recensement et les contributions des communautés locales. Cette initiative est cruciale alors que les s…

LocalBench is a newly introduced benchmark aimed at evaluating large language models (LLMs) on county-level local knowledge and reasoning in the United States. It addresses the inadequacies of existing benchmarks that primarily focus on macro-scale geographic tasks. LocalBench comprises 14,782 validated question-answer pairs across 526 U.S. counties, integrating diverse data sources such as Census statistics and local community inputs. This initiative is crucial as AI systems increasingly require nuanced understanding of local dynamics and governance.

LocalBench: Benchmarking LLMs on County-Level Local Knowledge and Reasoning

<p class="feature-image"><a href="https://petapixel.com/2025/11/19/students-picked-a-dramatic-nebula-as-the-gemini-north-telescopes-25th-birthday-photo-subject/"><img width="1600" height="840" src="https://petapixel.com/assets/uploads/2025/11/noirlab2529a-1600x840-1.jpg" class="attachment-card-large size-card-large wp-post-image" alt="Tall, dark, pillar-like clouds of gas and dust stretch upward against a bright, reddish-orange nebula background dotted with numerous bright stars and glowing points of light." decoding="async" /></a></p><p>Students in Hawaiʻi have played a central role in celebrating the 25th anniversary of the International Gemini Observatory, selecting the target for a new Gemini North telescope image and giving the final result a name that reflects the islands’ deep cultural and scientific heritage.</p>
<p class="read-more">[<a href="https://petapixel.com/2025/11/19/students-picked-a-dramatic-nebula-as-the-gemini-north-telescopes-25th-birthday-photo-subject/">Read More</a>]</p>

لعب الطلاب في هاواي دورًا مركزيًا في الاحتفال بالذكرى الخامسة والعشرين لمرصد جمنين الدولي، حيث اختاروا سديمًا دراماتيكيًا كموضوع لصورة جديدة تم التقاطها بواسطة تلسكوب جمنين الشمالي. يعكس اسم السديم المختار التراث الثقافي والعلمي العميق للجزر.

Estudiantes en Hawái han desempeñado un papel central en la celebración del 25 aniversario del Observatorio Internacional Gemini, seleccionando una nebulosa dramática como el objetivo para una nueva imagen del telescopio Gemini North. El nombre de la nebulosa elegida refleja la rica herencia cultural y científica de las islas.

Des étudiants d'Hawaï ont joué un rôle central dans la célébration du 25e anniversaire de l'Observatoire international Gemini en choisissant une nébuleuse dramatique comme sujet d'une nouvelle image prise par le télescope Gemini North. Le nom de la nébuleuse choisie reflète le riche patrimoine culturel et scientifique des îles.

Students in Hawaiʻi have contributed significantly to the 25th anniversary celebration of the International Gemini Observatory by selecting a dramatic nebula as the subject for a new image taken by the Gemini North telescope. The chosen nebula's name reflects the rich cultural and scientific heritage of the islands.

Students Picked a Dramatic Nebula as the Gemini North Telescope’s 25th Birthday Photo Subject

<p class="feature-image"><a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/"><img width="1600" height="840" src="https://petapixel.com/assets/uploads/2025/11/wolve-tool-use.jpg" class="attachment-card-large size-card-large wp-post-image" alt="A wolf stands at the edge of a rocky shoreline, holding an orange and white fishing bobber in its mouth. A fishing net and additional gear are lying on the ground nearby. Rippling water is in the background." decoding="async" fetchpriority="high" /></a></p><p>Remote cameras have captured footage of wild wolves pulling crab traps out of the sea by their lines to eat the bait inside -- in the first evidence of possible tool use by the canines.</p>
<p class="read-more">[<a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/">Read More</a>]</p>

التقطت الكاميرات عن بُعد لقطات لذئاب برية تستخدم تقنية تتضمن سحب فخاخ السلطعون من البحر للوصول إلى الطعم داخلها. وهذا يمثل أول دليل موثق على استخدام محتمل للأدوات من قبل هذه الكلاب.

Cámaras remotas han grabado a lobos salvajes utilizando una técnica que consiste en sacar trampas de cangrejos del mar para acceder al cebo en su interior. Esto marca la primera evidencia documentada de un posible uso de herramientas por parte de estos caninos.

Des caméras à distance ont enregistré des loups sauvages utilisant une technique qui consiste à tirer des pièges à crabes de la mer pour accéder à l'appât à l'intérieur. Cela marque la première preuve documentée d'un potentiel usage d'outils par ces canidés.

Remote cameras have recorded wild wolves using a technique that involves pulling crab traps from the sea to access the bait inside. This marks the first documented evidence of potential tool use by these canines.

Astronaut Captures Spectacular Aurora Lights Dancing Above the United States

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