arXiv:2511.09127v1 Announce Type: cross 
Abstract: Advances in Multimodal Large Language Models have significantly enhanced Graphical User Interface (GUI) automation. Equipping GUI agents with reliable episodic reasoning capabilities is essential for bridging the gap between users' concise task descriptions and the complexities of real-world execution. Current methods integrate Reinforcement Learning (RL) with System-2 Chain-of-Thought, yielding notable gains in reasoning enhancement. For long-horizon GUI tasks, historical interactions connect each screen to the goal-oriented episode chain, and effectively leveraging these clues is crucial for the current decision. However, existing native GUI agents exhibit weak short-term memory in their explicit reasoning, interpreting the chained interactions as discrete screen understanding, i.e., unawareness of the historical interactions within the episode. This history-agnostic reasoning challenges their performance in GUI automation. To alleviate this weakness, we propose a History-Aware Reasoning (HAR) framework, which encourages an agent to reflect on its own errors and acquire episodic reasoning knowledge from them via tailored strategies that enhance short-term memory in long-horizon interaction. The framework mainly comprises constructing a reflective learning scenario, synthesizing tailored correction guidelines, and designing a hybrid RL reward function. Using the HAR framework, we develop a native end-to-end model, HAR-GUI-3B, which alters the inherent reasoning mode from history-agnostic to history-aware, equipping the GUI agent with stable short-term memory and reliable perception of screen details. Comprehensive evaluations across a range of GUI-related benchmarks demonstrate the effectiveness and generalization of our method.

أدت التطورات الأخيرة في نماذج اللغة متعددة الوسائط إلى تحسين أتمتة واجهات المستخدم الرسومية (GUI). ومع ذلك، تواجه الوكلاء الحاليون في واجهات المستخدم الرسومية صعوبات في الذاكرة قصيرة المدى في تفكيرهم، مما يؤدي إلى تنفيذ غير فعال للمهام. لمعالجة ذلك، تم اقتراح إطار عمل جديد يسمى التفكير الواعي بالتاريخ (HAR)، الذي يعزز التفكير الإيبيزودي للوكلاء من خلال التفكير في الأخطاء السابقة. هذا التطور مهم لسد الفجوة بين تعليمات المستخدم وتعقيدات المهام في العالم الحقيقي.

Los recientes avances en Modelos de Lenguaje Multimodal han mejorado la automatización de Interfaces Gráficas de Usuario (GUI). Sin embargo, los agentes GUI existentes enfrentan dificultades con la memoria a corto plazo en su razonamiento, lo que lleva a una ejecución ineficaz de tareas. Para abordar esto, se ha propuesto un nuevo marco de Razonamiento Consciente de la Historia (HAR), que mejora el razonamiento episódico de los agentes al reflexionar sobre errores pasados. Este desarrollo es crucial para cerrar la brecha entre las instrucciones del usuario y las complejidades de las tareas en el mundo real.

Les récentes avancées dans les modèles de langage multimodaux ont amélioré l'automatisation des interfaces graphiques (GUI). Cependant, les agents GUI existants ont des difficultés avec la mémoire à court terme dans leur raisonnement, ce qui entraîne une exécution inefficace des tâches. Pour remédier à cela, un nouveau cadre de raisonnement conscient de l'histoire (HAR) a été proposé, qui améliore le raisonnement épisodique des agents en réfléchissant sur les erreurs passées. Ce développement est crucial pour combler le fossé entre les instructions des utilisateurs et les complexités des tâches dans le monde réel.

Recent advancements in Multimodal Large Language Models have improved Graphical User Interface (GUI) automation. However, existing GUI agents struggle with short-term memory in reasoning, leading to ineffective task execution. To address this, a new History-Aware Reasoning (HAR) framework has been proposed, which enhances agents' episodic reasoning by reflecting on past errors. This development is crucial for bridging the gap between user instructions and real-world task complexities.

History-Aware Reasoning for GUI Agents

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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:2505.10769v2 Announce Type: replace 
Abstract: Accurate segmentation of regions of interest in biomedical images holds substantial value in image analysis. Although several foundation models for biomedical segmentation have currently achieved excellent performance on certain datasets, they typically demonstrate sub-optimal performance on unseen domain data. We owe the deficiency to lack of vision-language knowledge before segmentation. Multimodal Large Language Models (MLLMs) bring outstanding understanding and reasoning capabilities to multimodal tasks, which inspires us to leverage MLLMs to inject Vision-Language Knowledge (VLK), thereby enabling vision models to demonstrate superior generalization capabilities on cross-domain datasets. In this paper, we propose a novel framework that seamlessly uses MLLMs to guide SAM in learning microscopy cross-domain data, unifying Segment Anything in Microscopy, named uLLSAM. Specifically, we propose the Vision-Language Semantic Alignment (VLSA) module, which injects VLK into Segment Anything Model (SAM). We find that after SAM receives global VLK prompts, its performance improves significantly, but there are deficiencies in boundary contour perception. Therefore, we further propose Semantic Boundary Regularization (SBR) to regularize SAM. Our method achieves performance improvements of 11.8% in SA across 9 in-domain microscopy datasets, achieving state-of-the-art performance. Our method also demonstrates improvements of 9.2% in SA across 10 out-of-domain datasets, exhibiting strong generalization capabilities. Code is available at https://github.com/ieellee/uLLSAM.

تناقش الورقة المعنونة 'توحيد نموذج تقسيم أي شيء في المجهر باستخدام المعرفة بين الرؤية واللغة' أهمية التقسيم الدقيق في الصور الطبية الحيوية. تبرز الورقة قيود النماذج الحالية في التعامل مع بيانات المجالات غير المرئية بسبب نقص المعرفة بين الرؤية واللغة. يقترح المؤلفون إطارًا جديدًا، uLLSAM، الذي يستخدم نماذج اللغة متعددة الوسائط (MLLMs) لتعزيز أداء التقسيم. تهدف هذه الطريقة إلى تحسين قدرات التعميم عبر مجموعات البيانات بين المجالات.

El artículo titulado 'Unifying Segment Anything in Microscopy with Vision-Language Knowledge' discute la importancia de la segmentación precisa en imágenes biomédicas. Destaca las limitaciones de los modelos existentes para manejar datos de dominio no vistos debido a la falta de conocimiento visión-lenguaje. Los autores proponen un nuevo marco, uLLSAM, que utiliza Modelos de Lenguaje Multimodal (MLLMs) para mejorar el rendimiento de la segmentación. Este enfoque busca mejorar las capacidades de generalización en conjuntos de datos inter-dominio.

L'article intitulé 'Unifying Segment Anything in Microscopy with Vision-Language Knowledge' aborde l'importance de la segmentation précise dans les images biomédicales. Il met en lumière les limites des modèles existants pour traiter des données de domaine non vues en raison d'un manque de connaissance vision-langage. Les auteurs proposent un nouveau cadre, uLLSAM, qui utilise des modèles de langage multimodaux (MLLMs) pour améliorer la performance de segmentation. Cette approche vise à améliorer les capacités de généralisation sur des ensembles de données inter-domaines.

The paper titled 'Unifying Segment Anything in Microscopy with Vision-Language Knowledge' discusses the importance of accurate segmentation in biomedical images. It highlights the limitations of existing models in handling unseen domain data due to a lack of vision-language knowledge. The authors propose a new framework, uLLSAM, which utilizes Multimodal Large Language Models (MLLMs) to enhance segmentation performance. This approach aims to improve generalization capabilities across cross-domain datasets, achieving notable performance improvements.

Unifying Segment Anything in Microscopy with Vision-Language Knowledge

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.

History-Aware Reasoning for GUI Agents

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