Search: “privacy”

DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection

arXiv:2511.13108v3 Announce Type: replace Abstract: The rapid progress of generative models such as GANs and diffusion models has led to the widespread proliferation of AI-generated images, raising co

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FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training

DGX agent

arXiv:2605.02337v1 Announce Type: cross Abstract: Federated Learning (FL) has gained significant attention in distributed machine learning by enabling collaborative model training across decentralized

model-releasesarxiv-cs-lg

5 May 2026

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar

Knowledge-Based Design Requirements for Generative Social Robots in Higher Education

arXiv:2602.12873v4 Announce Type: replace-cross Abstract: Generative social robots (GSRs) powered by large language models enable adaptive, conversational tutoring but also introduce risks such as mis

LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions

arXiv:2605.00879v1 Announce Type: new Abstract: Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other

researcharxiv-cs-ro

MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio

model-releasesarxiv-cs-cl

arXiv:2605.00969v1 Announce Type: cross Abstract: We present MedMosaic, a medical audio question-answering dataset designed to benchmark language and audio reasoning models under realistic clinical co

Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models

arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp

Representation learning from OCT images

arXiv:2605.02589v1 Announce Type: new Abstract: Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visualiz

safetyarxiv-cs-cv

Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning

arXiv:2605.01041v1 Announce Type: cross Abstract: In the envisioned future dense urban airspace, multiple companies will operate heterogeneous fleets of small unmanned aerial systems (sUASs), where ea

safetyarxiv-cs-lg

Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection

model-releasesarxiv-cs-lg

arXiv:2605.02860v1 Announce Type: cross Abstract: Cross-language code clone detection (X-CCD) is challenging because semantically equivalent programs written in different languages often share little

SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions

arXiv:2507.19673v3 Announce Type: replace Abstract: Accurate pain assessment in patients with limited ability to communicate, such as older adults with severe dementia, represents a critical healthcar

safetyarxiv-cs-cv

The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't

model-releasesarxiv-cs-cl

arXiv:2605.01771v1 Announce Type: new Abstract: An auditor instructs an AI assistant: 'open each file individually using the Read tool -- no scripts, no agents.' The AI replies 'Yes' -- then issues a

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

arXiv:2602.12361v2 Announce Type: replace Abstract: Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in f

safetyarxiv-cs-cv

CollaFuse: Collaborative Diffusion Models

arXiv:2406.14429v3 Announce Type: replace-cross Abstract: In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic ima

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration

arXiv:2605.00578v1 Announce Type: new Abstract: Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-w

local-aiarxiv-cs-cv

Federated Weather Modeling on Sensor Data

arXiv:2605.00322v1 Announce Type: new Abstract: Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including gr

Firestore at Next '26: Unlock agentic development, search and MongoDB compatibility

model-releasesgoogle-cloud-ai

In the era of AI agents, the distance between a big idea and a working application has never been shorter. As we lean more heavily on agents to help us build applications, a critical question remains:

How I built a free, local AI powerhouse in 10 days (Ollama + Gemma 4 + Claude Cowork 3P + Browserless)

This post documents a 10-day project to build a local AI system using open-source tools and models, specifically combining Ollama (a local LLM framework), Gemma 4 (a language model), Claude Cowork 3P,

model-releasesr-ollama

Network Digital Untwinning: Towards Backward Optimization of Digital Twins

arXiv:2605.00169v1 Announce Type: cross Abstract: Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reli

SynQuE: Estimating Synthetic Dataset Quality Without Annotations

arXiv:2511.03928v5 Announce Type: replace Abstract: We introduce and formalize the Synthetic Dataset Quality Estimation (SynQuE) problem: ranking synthetic datasets by their expected real-world task p

AI will create more jobs than any other technology in history. The doomers' fundamental error isn't just the lump of labor fallacy. It's dee…

AI will create more jobs than any other technology in history. The doomers' fundamental error isn't just the lump of labor fallacy. It's deeper than that. They assume a finite problem space. This is t

safetyyann-lecun--x

3 May 2026

If GitHub were built in: Japan 🇯🇵 China 🇨🇳 North Korea 🇰🇵 The EU 🇪🇺

This post is a humorous thought experiment comparing how GitHub's features, policies, and design would differ if the platform were developed in various countries and regions, each reflecting their dis

researchdavid-ha--x

3 May 2026

Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption

arXiv:2604.27245v1 Announce Type: cross Abstract: Generative AI has rapidly entered education through free consumer tools, outpacing the ability of schools and universities to respond. Now a new wave

Hardware

All the Doomers and hawks are lining up behind this distillation 'attack' farce because they want to see open source banned. It's really as …

All the Doomers and hawks are lining up behind this distillation 'attack' farce because they want to see open source banned. It's really as simple as that. They want to take away your right to choose,

hardwareyann-lecun--x

EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

arXiv:2603.13566v2 Announce Type: replace-cross Abstract: Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare

Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents

arXiv:2604.27143v1 Announce Type: cross Abstract: Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using cloud-based

local-aiarxiv-cs-ai

FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

arXiv:2604.28024v1 Announce Type: new Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learnin

OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment

model-releasesarxiv-cs-ai

arXiv:2506.22500v2 Announce Type: replace-cross Abstract: Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs

Policy-Grounded Safety Evaluation of 20 Large Language Models

arXiv:2507.14719v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. T

Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors

arXiv:2604.27426v1 Announce Type: cross Abstract: Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local offline

local-aiarxiv-cs-ai

Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework

arXiv:2604.27574v1 Announce Type: cross Abstract: Channel fingerprint (CF) is considered a key enabler for facilitating the acquisition of channel state information (CSI) in massive multiple-input mul

researcharxiv-cs-ai

WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2604.27629v1 Announce Type: new Abstract: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models. To address data scarcity in semiconduct

Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection

arXiv:2509.11058v2 Announce Type: replace Abstract: Zero-Shot Video Anomaly Detection (ZS-VAD) requires temporally localizing anomalies without target domain training data, which is a crucial task due

local-aiarxiv-cs-cv

Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats

arXiv:2604.25920v1 Announce Type: cross Abstract: Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tu

applicationsarxiv-cs-ai

Cloud CISO Perspectives: At Next ‘26, why we’re multicloud and multi-AI

model-releasesgoogle-cloud-ai

Welcome to the second Cloud CISO Perspectives for April 2026. Today, Francis deSouza, COO Google Cloud and President, Security Products, explains why Google is multicloud and multi-AI, straight from N

Tutorials

CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

arXiv:2604.26363v1 Announce Type: new Abstract: Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple dec

tutorialsarxiv-cs-cv

Data-Centric Foundation Models in Computational Healthcare: A Survey

arXiv:2401.02458v3 Announce Type: replace-cross Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. Th

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

arXiv:2505.02077v2 Announce Type: replace-cross Abstract: AI agents are beginning to interact with each other directly and across internet platforms and physical environments, creating security challe

Quantamination: Dynamic Quantization Leaks Your Data Across the Batch

arXiv:2604.26505v1 Announce Type: cross Abstract: Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static qu

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data

arXiv:2604.26116v1 Announce Type: new Abstract: Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server whil

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning

arXiv:2604.26388v1 Announce Type: cross Abstract: Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federa

Tatemae: Detecting Alignment Faking via Tool Selection in LLMs

arXiv:2604.26511v1 Announce Type: cross Abstract: Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences o

What Google Cloud announced in AI this month

model-releasesgoogle-cloud-ai

Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more. We host

CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.25376v1 Announce Type: new Abstract: Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data

Diverse Image Priors for Black-box Data-free Knowledge Distillation

arXiv:2604.25794v1 Announce Type: cross Abstract: Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in

FCMBench-Video: Benchmarking Document Video Intelligence

model-releasesarxiv-cs-cv

arXiv:2604.25186v1 Announce Type: new Abstract: Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and eviden

Forward and backward benchmark results across common configurations.

The document titled 'Forward and backward benchmark results across common configurations' is inaccessible because JavaScript is disabled in the browser. Users are prompted to enable JavaScript or swit

model-releasesqwen--x

Intrinsic Mutual Information as a Modulator for Preference Optimization

arXiv:2604.24804v1 Announce Type: cross Abstract: Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models

researcharxiv-cs-cl