Search: “privacy”

Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients

model-releasesarxiv-cs-cv

arXiv:2606.24774v1 Announce Type: new Abstract: Vision-Language Large Models (VLLMs) trained on massive crawled corpora raise pressing copyright and data-provenance concerns. These concerns are partic

24 Jun 2026

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The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences

DGX agent

arXiv:2606.24104v1 Announce Type: cross Abstract: Generative artificial intelligence(GenAI) is reshaping learning in higher education, with particularly pronounced implications for the humanities and

applicationsarxiv-cs-ai

24 Jun 2026

C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model

arXiv:2606.23473v1 Announce Type: new Abstract: Universal segmentation models exhibit significant potential for diverse tasks involving different imaging modalities and segmentation objectives. Task-I

Chehre: An Emoji-Prompted Video Dataset for Perceptually Diverse Facial Expression Recognition

model-releasesarxiv-cs-cv

arXiv:2606.21657v1 Announce Type: new Abstract: Facial expressions are nonverbal social signals used in human interaction, but facial expression recognition datasets often focus on static images, basi

Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences

arXiv:2606.23500v1 Announce Type: cross Abstract: Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research

applicationsarxiv-cs-lg

Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

arXiv:2606.22496v1 Announce Type: cross Abstract: Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however,

local-aiarxiv-cs-lg

Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead

arXiv:2602.07049v2 Announce Type: replace Abstract: Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardwar

safetyarxiv-cs-cv

FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs

arXiv:2606.22875v1 Announce Type: new Abstract: Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful gen

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training

arXiv:2603.06467v2 Announce Type: replace Abstract: Radiology foundation models (RFMs) have largely inherited the scale-first recipe of natural-image vision--language pre-training. This recipe is diff

local-aiarxiv-cs-cv

Object-Centric Dataset Resources for Constrained-Data Image Generation and Augmentation

arXiv:2606.21113v1 Announce Type: new Abstract: Object-centric image generation is important in settings with few labeled examples, including pedestrian analysis in smart-city scenes, traffic-sign ins

OFMU: Optimization-Driven Framework for Machine Unlearning

arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy

safetyarxiv-cs-lg

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning

arXiv:2606.22878v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed at the network edge to provide pervasive generative AI services, decentralized federated learn

Robust Zero-Shot Generalization for Open-Vocabulary Action Recognition via Task Arithmetic

arXiv:2606.20734v1 Announce Type: new Abstract: Open Vocabulary Action Recognition (OVAR) enables the recognition of novel actions by leveraging vision-language representations, overcoming the limitat

applicationsarxiv-cs-cv

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction

arXiv:2606.22606v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deploymen

Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection

arXiv:2509.21606v3 Announce Type: replace Abstract: Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting

researcharxiv-cs-lg

Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design

arXiv:2606.22782v1 Announce Type: new Abstract: The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device ma

APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

arXiv:2606.11553v1 Announce Type: new Abstract: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

arXiv:2606.11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new

Continual Learning with Support Boundary Experience Blending

arXiv:2507.23534v3 Announce Type: replace-cross Abstract: Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience

Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles

model-releasesarxiv-cs-cl

arXiv:2606.12088v1 Announce Type: new Abstract: Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information

Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models

arXiv:2606.12114v1 Announce Type: new Abstract: Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information

researcharxiv-cs-cl

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

model-releasesarxiv-cs-cl

arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generati

Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions

arXiv:2604.25018v2 Announce Type: replace-cross Abstract: The Internet of Everything (IoE) represents an evolution of the Internet of Things (IoT) by integrating people, data, processes, and things in

MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation

arXiv:2606.11416v1 Announce Type: cross Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, a

Agents

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

arXiv:2509.23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and a

agentsarxiv-cs-ai

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

arXiv:2606.11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples

Active-Passive Federated Learning for Vertically Partitioned Multi-view Data

arXiv:2409.04111v2 Announce Type: replace Abstract: Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while pre

applicationsarxiv-cs-lg

Boosting ECG Classification Performance by Pre-training with Synthesized Data

arXiv:2606.10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training. In the medical domain, acquiring large-scale data is often ch

Tutorials

Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

arXiv:2606.10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two

tutorialsarxiv-cs-ai

Deployment-Time Memorization in Foundation-Model Agents

arXiv:2606.10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time fun

Agents

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

arXiv:2606.10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervas

agentsarxiv-cs-ai

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning

arXiv:2606.10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contribut

local-aiarxiv-cs-ai

++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

arXiv:2606.10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biome

researcharxiv-cs-ai

Quantifying Perception-Based Student Success with Generative AI: An Exploratory Monte Carlo Simulation

arXiv:2507.01062v4 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) tools such as ChatGPT have attracted growing attention in higher education, particularly in relatio

The Emergence of Reproducibility and Generalizability in Diffusion Models

arXiv:2310.05264v5 Announce Type: replace-cross Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as 'consistent model reproducibility': g

AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond

arXiv:2606.07544v1 Announce Type: cross Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fa

safetyarxiv-cs-ai

AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning

arXiv:2606.09447v1 Announce Type: new Abstract: We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles. Major cloud platforms encomp

Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning

arXiv:2601.22669v3 Announce Type: replace Abstract: Federated Learning (FL) facilitates decentralized collaborative learning without transmitting raw data. However, reliance on fixed global rounds or

researcharxiv-cs-lg

Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense

arXiv:2606.08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adj

safetyarxiv-cs-lg

Federated Large Language Models: Current Progress and Future Directions

arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data col

local-aiarxiv-cs-lg

GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development

arXiv:2606.09681v1 Announce Type: new Abstract: Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signa

local-aiarxiv-cs-cv

Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation

arXiv:2606.07660v1 Announce Type: new Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on

local-aiarxiv-cs-cv

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation

arXiv:2606.07688v1 Announce Type: cross Abstract: Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical

safetyarxiv-cs-ai

Agents

Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces

arXiv:2606.09227v1 Announce Type: cross Abstract: The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD),

agentsarxiv-cs-ai

Federated Foundation Models over Vehicular Networks

arXiv:2606.06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular

applicationsarxiv-cs-lg

PromptPrint: Behavioral Biometrics Through Natural Language Prompting in LLMs

model-releasesarxiv-cs-cl

arXiv:2606.06755v1 Announce Type: new Abstract: Authorship attribution research has traditionally focused on long-form, expressive texts; however, interactions with large language models (LLMs) are ty

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606.07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owne

Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning

arXiv:2509.05316v2 Announce Type: replace-cross Abstract: A conventional LLM Unlearning setting consists of two subsets -'forget' and 'retain', with the objectives of removing the undesired knowledge