Search: “privacy”

Domain-Adapted Small Language Models for Reliable Clinical Triage

arXiv:2604.26766v1 Announce Type: cross Abstract: Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-

30 Apr 2026

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Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent

DGX agent

arXiv:2604.26197v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.

agentsarxiv-cs-lg

30 Apr 2026

A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards

arXiv:2505.13766v5 Announce Type: replace-cross Abstract: Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance

safetyarxiv-cs-cl

29 Apr 2026

CiteRadar: A Citation Intelligence Platform for Researcher Profiling and Geographic Visualization

arXiv:2604.25057v1 Announce Type: new Abstract: Understanding the geographic reach and community structure of one's scholarly citations is increasingly valuable for career development, grant applicati

29 Apr 2026

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning

arXiv:2604.23386v1 Announce Type: cross Abstract: Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder envi

Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters

arXiv:2604.24710v1 Announce Type: new Abstract: Objective. Clinical AI documentation systems require evaluation methodologies that are clinically valid, economically viable, and sensitive to iterative

Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training

arXiv:2303.05330v1 Announce Type: cross Abstract: Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resource

applicationsarxiv-cs-ai

Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach

arXiv:2602.04753v2 Announce Type: replace-cross Abstract: An exponential growth of Machine Learning and its Generative AI applications brings with it significant security challenges, often referred to

applicationsarxiv-cs-ai

Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency

arXiv:2501.02673v4 Announce Type: replace Abstract: Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determin

Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

arXiv:2604.22885v1 Announce Type: cross Abstract: Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modali

Identity-Decoupled Anonymization for Visual Evidence in Multi-modal Retrieval-Augmented Generation

arXiv:2604.23584v1 Announce Type: new Abstract: Multi-modal retrieval-augmented generation (MRAG) systems retrieve visual evidence from large image corpora to ground the responses of large multi-modal

researcharxiv-cs-cv

Tutorials

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations

arXiv:2407.14974v2 Announce Type: replace-cross Abstract: Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relat

tutorialsarxiv-cs-ai

Protecting the Trace: A Principled Black-Box Approach Against Distillation Attacks

arXiv:2604.23238v1 Announce Type: cross Abstract: Frontier models push the boundaries of what is learnable at extreme computational costs, yet distillation via sampling reasoning traces exposes closed

Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers

arXiv:2604.23046v1 Announce Type: new Abstract: We argue that current definitions of machine unlearning are underspecified for second-order optimizers. We compare first-order and second-order learners

Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion

arXiv:2604.23094v1 Announce Type: new Abstract: The real-world adoption of portrait relighting is hindered by dataset domain gaps, camera sensitivity, and computational costs. We address these challen

applicationsarxiv-cs-cv

TRINITY: An Evolved LLM Coordinator

arXiv:2512.04695v3 Announce Type: replace Abstract: Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. Trinity addresses this

AgentMark: Utility-Preserving Behavioral Watermarking for Agents

arXiv:2601.03294v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly deployed to autonomously solve complex tasks, raising urgent needs for IP protection and regulatory provenan

Local Ai

An LLM-Driven Closed-Loop Autonomous Learning Framework for Robots Facing Uncovered Tasks in Open Environments

arXiv:2604.22199v1 Announce Type: cross Abstract: Autonomous robots operating in open environments need the ability to continuously handle tasks that are not covered by predefined local methods. Howev

local-aiarxiv-cs-ai

Model Releases

Asymmetric Goal Drift in Coding Agents Under Value Conflict

model-releasesarxiv-cs-ai

arXiv:2603.03456v2 Announce Type: replace Abstract: Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate

CRAFT: Clustered Regression for Adaptive Filtering of Training data

arXiv:2604.22693v1 Announce Type: cross Abstract: Selecting a small, high-quality subset from a large corpus for fine-tuning is increasingly important as corpora grow to tens of millions of datapoints

Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification

arXiv:2603.00177v2 Announce Type: replace-cross Abstract: The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are inc

Local Ai

How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications

arXiv:2604.22679v1 Announce Type: cross Abstract: The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses includin

local-aiarxiv-cs-ai

SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking

arXiv:2604.22438v1 Announce Type: cross Abstract: Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing appr

TRINITY: An Evolved LLM Coordinator https://arxiv.org/abs/2512.04695

TRINITY is a large language model coordinator system that evolves and improves its approach to orchestrating multiple LLMs or reasoning processes. The system likely uses evolutionary or adaptive mecha

researchdavid-ha--x

26 Apr 2026

Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model

arXiv:2604.20907v1 Announce Type: cross Abstract: We study the community detection problem in the non-uniform hypergraph stochastic block model (HSBM), where hyperedges of varying sizes coexist. This

Addressing Image Authenticity When Cameras Use Generative AI

arXiv:2604.21879v1 Announce Type: cross Abstract: The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared on

Certified Coil Geometry Learning for Short-Range Magnetic Actuation and Spacecraft Docking Application

arXiv:2507.03806v3 Announce Type: replace-cross Abstract: This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and ex

Model Releases

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents

model-releasesarxiv-cs-cl

arXiv:2604.21308v1 Announce Type: cross Abstract: Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a us

StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching

arXiv:2601.09056v3 Announce Type: replace-cross Abstract: Stylometry--the identification of an author through analysis of a text's style (i.e., authorship attribution)--serves many constructive purpos

researcharxiv-cs-cl

The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI

arXiv:2503.07341v2 Announce Type: replace-cross Abstract: Recent advances in artificial intelligence (AI) have led to a wide range of predictions about its long-term impact on humanity. A central focu

Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

arXiv:2603.24111v3 Announce Type: replace-cross Abstract: The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integr

applicationsarxiv-cs-lg

UbiQVision: Quantifying Uncertainty in XAI for Image Recognition

arXiv:2512.20288v2 Announce Type: replace-cross Abstract: Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is drive

Unbiased Prevalence Estimation with Multicalibrated LLMs

arXiv:2604.21549v1 Announce Type: new Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

arXiv:2507.16433v2 Announce Type: replace-cross Abstract: Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portf

Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?

arXiv:2604.19785v1 Announce Type: cross Abstract: Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messag

Local Ai

CEDAR: Context Engineering for Agentic Data Science

arXiv:2601.06606v2 Announce Type: replace-cross Abstract: We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underex

local-aiarxiv-cs-ai

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints

arXiv:2604.20031v1 Announce Type: cross Abstract: We consider what we refer to as {Decision-Focused Federated Learning (DFFL)} framework, i.e., a predict-then-optimize approach employed by a collectio

agentsarxiv-cs-lg

LLMs Can Get 'Brain Rot': A Pilot Study on Twitter/X

arXiv:2510.13928v2 Announce Type: replace-cross Abstract: We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language mode

Membership Inference for Contrastive Pre-training Models with Text-only PII Queries

arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the

Onyx: Cost-Efficient Disk-Oblivious ANN Search

arXiv:2604.20401v1 Announce Type: cross Abstract: Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environme

Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital

arXiv:2604.19774v1 Announce Type: cross Abstract: Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (

Model Releases

Rashomon Sets and Model Multiplicity in Federated Learning

model-releasesarxiv-cs-lg

arXiv:2602.09520v2 Announce Type: replace Abstract: The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision

Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transport

arXiv:2510.01706v2 Announce Type: replace-cross Abstract: Standard representational similarity methods align each layer of a network to its best match in another independently, producing asymmetric re

SMART: A Spectral Transfer Approach to Multi-Task Learning

arXiv:2604.20161v1 Announce Type: new Abstract: Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning c

Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection

arXiv:2604.20549v1 Announce Type: cross Abstract: As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality

Tutorials

Accelerating Optimization and Machine Learning through Decentralization

arXiv:2604.19518v1 Announce Type: new Abstract: Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local d

tutorialsarxiv-cs-lg

22 Apr 2026

An AI Agent Execution Environment to Safeguard User Data

arXiv:2604.19657v1 Announce Type: cross Abstract: AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g., pers

22 Apr 2026

Co-Refine: AI-Powered Tool Supporting Qualitative Analysis

arXiv:2604.19309v1 Announce Type: cross Abstract: Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes o