arXiv:2511.11232v1 Announce Type: new 
Abstract: The generalization of 3D deep learning across multiple domains remains limited by the limited scale of existing datasets and the high heterogeneity of multi-source point clouds. Point clouds collected from different sensors (e.g., LiDAR scans and mesh-derived point clouds) exhibit substantial discrepancies in density and noise distribution, resulting in negative transfer during multi-domain fusion. Most existing approaches focus exclusively on either domain-aware or domain-general features, overlooking the potential synergy between them. To address this, we propose DoReMi (Domain-Representation Mixture), a Mixture-of-Experts (MoE) framework that jointly models Domain-aware Experts branch and a unified Representation branch to enable cooperative learning between specialized and generalizable knowledge. DoReMi dynamically activates domain-aware expert branch via Domain-Guided Spatial Routing (DSR) for context-aware expert selection and employs Entropy-Controlled Dynamic Allocation (EDA) for stable and efficient expert utilization, thereby adaptively modeling diverse domain distributions. Complemented by a frozen unified representation branch pretrained through robust multi-attribute self-supervised learning, DoReMi preserves cross-domain geometric and structural priors while maintaining global consistency. We evaluate DoReMi across multiple 3D understanding benchmarks. Notably, DoReMi achieves 80.1% mIoU on ScanNet Val and 77.2% mIoU on S3DIS, demonstrating competitive or superior performance compared to existing approaches, and showing strong potential as a foundation framework for future 3D understanding research. The code will be released soon.

يقدم المقال DoReMi، وهو إطار عمل جديد يهدف إلى تحسين تعميم التعلم العميق ثلاثي الأبعاد عبر مجالات متعددة. يتناول التحديات التي تطرحها النطاقات المحدودة لمجموعات البيانات الحالية والتباين العالي في سحب النقاط متعددة المصادر، مما قد يؤدي إلى نقل سلبي أثناء دمج المجالات. يستخدم DoReMi نهج مزيج الخبراء، حيث يجمع بين الميزات الواعية بالمجال والميزات العامة لتسهيل التعلم التعاوني، محققًا في النهاية 80.1% من متوسط تقاطع الاتحاد على مجموعة التحقق من ScanNet.

El artículo presenta DoReMi, un nuevo marco destinado a mejorar la generalización del aprendizaje profundo 3D en múltiples dominios. Aborda los desafíos que presentan la escala limitada de los conjuntos de datos existentes y la alta heterogeneidad de los nubes de puntos de múltiples fuentes, que pueden resultar en una transferencia negativa durante la fusión entre dominios. DoReMi emplea un enfoque de Mezcla de Expertos, combinando características conscientes del dominio y generalizables para facilitar el aprendizaje cooperativo, logrando finalmente un 80.1% de mIoU en el conjunto de validació…

L'article présente DoReMi, un nouveau cadre visant à améliorer la généralisation de l'apprentissage profond 3D à travers divers domaines. Il aborde les défis posés par l'échelle limitée des ensembles de données existants et l'hétérogénéité des nuages de points multi-sources, qui peuvent entraîner un transfert négatif lors de la fusion multi-domaines. DoReMi utilise une approche Mixture-of-Experts, combinant des caractéristiques conscientes du domaine et généralisables pour faciliter l'apprentissage coopératif, atteignant finalement un mIoU de 80,1 % sur l'ensemble de validation ScanNet.

The article introduces DoReMi, a new framework aimed at enhancing the generalization of 3D deep learning across various domains. It addresses the challenges posed by the limited scale of existing datasets and the heterogeneity of multi-source point clouds, which can lead to negative transfer during multi-domain fusion. DoReMi employs a Mixture-of-Experts approach, combining domain-aware and generalizable features to facilitate cooperative learning, ultimately achieving an 80.1% mean Intersection over Union (mIoU) on the ScanNet validation set.

DoReMi: A Domain-Representation Mixture Framework for Generalizable 3D Understanding

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<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

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Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

The most interesting aspect of the latest Even Realities glasses may be their limitations.

I've tried several AI smart glasses (including Meta Ray-Bans) in 2025 - these are the most comfortable

arXiv:2511.07947v2 Announce Type: replace-cross 
Abstract: Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through black-box queries. Model watermarking counters MEAs by embedding forensic markers for ownership verification. Current black-box watermarks prioritize MEA survival through representation entanglement, yet inadequately explore resilience against sequential MEAs and removal attacks. Our study reveals that this risk is underestimated because existing removal methods are weakened by entanglement. To address this gap, we propose Watermark Removal attacK (WRK), which circumvents entanglement constraints by exploiting decision boundaries shaped by prevailing sample-level watermark artifacts. WRK effectively reduces watermark success rates by at least 88.79% across existing watermarking benchmarks.
  For robust protection, we propose Class-Feature Watermarks (CFW), which improve resilience by leveraging class-level artifacts. CFW constructs a synthetic class using out-of-domain samples, eliminating vulnerable decision boundaries between original domain samples and their artifact-modified counterparts (watermark samples). CFW concurrently optimizes both MEA transferability and post-MEA stability. Experiments across multiple domains show that CFW consistently outperforms prior methods in resilience, maintaining a watermark success rate of at least 70.15% in extracted models even under the combined MEA and WRK distortion, while preserving the utility of protected models.

Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks

arXiv:2511.11688v1 Announce Type: new 
Abstract: Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

تناقش الورقة المعنونة 'تحسين الجدول الهرمي لعملية أخذ العينات السريعة والموثوقة لنماذج الانتشار' التقدم في النماذج الاحتمالية للانتشار، المعروفة بدقتها التوليدية ولكن تواجه تحديات بسبب بطء عمليات أخذ العينات. يقدم المؤلفون استراتيجية جديدة بدون تدريب تُسمى تحسين الجدول الزمني، تهدف إلى تحسين توزيع خطوات الزمن لتعزيز جودة العينات مع الالتزام بمبادئ الفعالية، والتكيف، والموثوقية، والكفاءة. يقدم المُحسّن الهرمي للجدول الزمني (HSO) إطار عمل جديدًا لتحسين ثنائي المستوى لمعالجة هذه التحديات.

El artículo titulado 'Optimización de Horarios Jerárquicos para un Muestreo Rápido y Robusto de Modelos de Difusión' discute los avances en modelos probabilísticos de difusión, conocidos por su fidelidad generativa pero que enfrentan desafíos debido a procesos de muestreo lentos. Los autores presentan una estrategia sin entrenamiento llamada Optimización de Horarios, que busca optimizar la distribución de pasos de tiempo para mejorar la calidad de las muestras, respetando principios de efectividad, adaptabilidad, robustez y eficiencia. El propuesto Optimizador de Horarios Jerárquicos (HSO) ofr…

L'article intitulé 'Optimisation Hiérarchique des Horaires pour un Échantillonnage Rapide et Robuste des Modèles de Diffusion' aborde les avancées dans les modèles probabilistes de diffusion, connus pour leur fidélité générative mais confrontés à des défis en raison de processus d'échantillonnage lents. Les auteurs introduisent une stratégie sans formation appelée Optimisation des Horaires, visant à optimiser la distribution des étapes de temps pour améliorer la qualité des échantillons tout en respectant les principes d'efficacité, d'adaptabilité, de robustesse et d'efficacité. Le nouvel Opti…

The paper titled 'Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling' discusses advancements in diffusion probabilistic models, which are known for their generative fidelity but face challenges due to slow sampling processes. The authors introduce a training-free strategy called Schedule Optimization, aimed at optimizing the distribution of timesteps to enhance sample quality while adhering to principles of effectiveness, adaptivity, robustness, and efficiency. The proposed Hierarchical-Schedule-Optimizer (HSO) offers a bi-level optimization framework to address th…

Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling

arXiv:2511.12098v1 Announce Type: new 
Abstract: Generating synthetic CT images from CBCT or MRI has a potential for efficient radiation dose planning and adaptive radiotherapy. However, existing CNN-based models lack global semantic understanding, while Transformers often overfit small medical datasets due to high model capacity and weak inductive bias. To address these limitations, we propose a DINOv3-Guided Cross Fusion (DGCF) framework that integrates a frozen self-supervised DINOv3 Transformer with a trainable CNN encoder-decoder. It hierarchically fuses global representation of Transformer and local features of CNN via a learnable cross fusion module, achieving balanced local appearance and contextual representation. Furthermore, we introduce a Multi-Level DINOv3 Perceptual (MLDP) loss that encourages semantic similarity between synthetic CT and the ground truth in DINOv3's feature space. Experiments on the SynthRAD2023 pelvic dataset demonstrate that DGCF achieved state-of-the-art performance in terms of MS-SSIM, PSNR and segmentation-based metrics on both MRI$\rightarrow$CT and CBCT$\rightarrow$CT translation tasks. To the best of our knowledge, this is the first work to employ DINOv3 representations for medical image translation, highlighting the potential of self-supervised Transformer guidance for semantic-aware CT synthesis. The code is available at https://github.com/HiLab-git/DGCF.

DoReMi: A Domain-Representation Mixture Framework for Generalizable 3D Understanding

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