Search: “governance”

An Agentic LLM-Based Framework for Population-Scale Mental Health Screening

arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from ele

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Local Ai

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

DGX agent

arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,

local-aiarxiv-cs-ai

14 May 2026

CHAL: Council of Hierarchical Agentic Language

arXiv:2605.12718v1 Announce Type: new Abstract: Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structu

Tutorials

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

arXiv:2509.23597v5 Announce Type: replace-cross Abstract: Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictab

tutorialsarxiv-cs-ai

Context Matters: Auditing Gender Bias in T2I Generation through Risk-Tiered Use-Case Profiles

arXiv:2605.13113v1 Announce Type: cross Abstract: Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting

safetyarxiv-cs-ai

Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning

model-releasesarxiv-cs-ai

arXiv:2605.13335v1 Announce Type: new Abstract: Embodied agents in household environments must plan under partial observation: they need to remember objects, track state changes, and recover when acti

Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation

arXiv:2605.12997v1 Announce Type: new Abstract: Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full o

Hardware

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later

arXiv:2509.19929v4 Announce Type: replace-cross Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of phy

hardwarearxiv-cs-lg

HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2605.13536v1 Announce Type: cross Abstract: High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR) -- latency and resource utilization --

Imposing Boundary Conditions on Neural Operators via Learned Function Extensions

model-releasesarxiv-cs-lg

arXiv:2602.04923v2 Announce Type: replace Abstract: Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general,

Improving Classifier-Free Guidance of Flow Matching via Manifold Projection

arXiv:2601.21892v2 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirica

safetyarxiv-cs-ai

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ

safetyarxiv-cs-ai

Multitask Multimodal Fusion with Tabular Foundation Models for Peak and Durability Prediction of Pertussis Booster Response

arXiv:2605.12852v1 Announce Type: new Abstract: Pertussis booster vaccination produces immune responses that vary widely across individuals in both peak magnitude and long-term durability. These two p

On the Limits of Latent Reuse in Diffusion Models

arXiv:2605.13448v1 Announce Type: cross Abstract: Diffusion models are often trained in low-dimensional latent spaces, which are then reused for related but shifted datasets. In this work, we study wh

Physics-informed neural particle flow for the Bayesian update step

arXiv:2602.23089v2 Announce Type: replace Abstract: The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filte

Radial Compensation: Fixing Radius Distortion in Chart-Based Generative Models on Riemannian Manifolds

arXiv:2511.14056v2 Announce Type: replace-cross Abstract: We study the base distribution in chart-based generative models on Riemannian manifolds. Standard methods sample in Euclidean tangent space an

researcharxiv-cs-ai

Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

arXiv:2604.01690v2 Announce Type: replace Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a

applicationsarxiv-cs-ai

Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale

arXiv:2605.13684v1 Announce Type: new Abstract: We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensi

Strategic PAC Learnability via Geometric Definability

arXiv:2605.13426v1 Announce Type: new Abstract: Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's dec

The Horizon Threshold in Cooperative Multi-Agent Reward-Free Exploration

arXiv:2602.01453v3 Announce Type: replace Abstract: We study cooperative multi-agent reinforcement learning in the setting of reward-free exploration, where multiple agents jointly explore an unknown

The Sample Complexity of Multiple Change Point Identification under Bandit Feedback

arXiv:2605.13252v1 Announce Type: cross Abstract: We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequent

Tutorials

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks

arXiv:2605.13690v1 Announce Type: cross Abstract: Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (

tutorialsarxiv-cs-ai

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

arXiv:2605.12764v1 Announce Type: cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learn

A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

arXiv:2605.12328v1 Announce Type: new Abstract: Data entry systems remain structurally vulnerable to categorical misclassifications, particularly in small and medium sized enterprises (SMEs). When nom

researcharxiv-cs-cl

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design

model-releasesarxiv-cs-lg

arXiv:2605.11759v1 Announce Type: cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeli

Beyond Similarity: Temporal Operator Attention for Time Series Analysis

arXiv:2605.11287v1 Announce Type: new Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue

Distributed Pose Graph Optimization via Continuous Riemannian Dynamics

model-releasesarxiv-cs-ro

arXiv:2605.11210v1 Announce Type: new Abstract: We present a framework for distributed Pose Graph Optimization (PGO) by formulating the problem as a second-order continuous-time dynamical system evolv

Dynamic Execution Commitment of Vision-Language-Action Models

arXiv:2605.11567v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level act

applicationsarxiv-cs-cv

EqOD: Symmetry-Informed Stability Selection for PDE Identification

arXiv:2605.11524v1 Announce Type: new Abstract: Data-driven identification of partial differential equations (PDEs) relies on sparse regression over a candidate library of differential operators, wher

Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies

arXiv:2605.11531v1 Announce Type: cross Abstract: Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits region

LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing

model-releasesarxiv-cs-cv

arXiv:2605.11508v1 Announce Type: new Abstract: Currently, there is a gap in the field of ultra-high-definition (UHD) video dehazing due to the lack of a benchmark for evaluation. Furthermore, existin

LucidFlux: Caption-Free Photo-Realistic Image Restoration via a Large-Scale Diffusion Transformer

arXiv:2509.22414v4 Announce Type: replace Abstract: Image restoration (IR) aims to recover images degraded by unknown mixtures while preserving semanticsconditions under which discriminative restorers

safetyarxiv-cs-cv

Maximin Robust Bayesian Experimental Design

arXiv:2603.14094v2 Announce Type: replace-cross Abstract: We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between

Minimax Rates and Spectral Distillation for Tree Ensembles

arXiv:2605.11841v1 Announce Type: cross Abstract: Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theore

Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning

arXiv:2411.19093v4 Announce Type: replace Abstract: Access to drinking water and sanitation services is essential for health and well-being, yet large global disparities persist. Sustainable Developme

researcharxiv-cs-cv

Local Ai

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models

arXiv:2605.11142v1 Announce Type: new Abstract: Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, commu

local-aiarxiv-cs-lg

Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward

arXiv:2605.12096v1 Announce Type: new Abstract: Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource

applicationsarxiv-cs-cl

SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels

arXiv:2605.11060v1 Announce Type: cross Abstract: Split Federated Learning (SplitFed) combines federated and split learning to preserve privacy while reducing client-side computation. However, in medi

applicationsarxiv-cs-cv

Steerable Neural ODEs on Homogeneous Spaces

arXiv:2605.11133v1 Announce Type: new Abstract: We introduce steerable neural ordinary differential equations on homogeneous spaces M=G/H. These models constitute a novel geometric extension of manifo

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models

arXiv:2605.10971v1 Announce Type: cross Abstract: Discrete diffusion language models (DLMs) generate text by iteratively denoising all positions in parallel, offering an alternative to autoregressive

researcharxiv-cs-cl

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting

arXiv:2605.11538v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However

safetyarxiv-cs-cl

The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested

arXiv:2605.11496v1 Announce Type: cross Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently represent them, and

Local Ai

A Physical Theory of Backpropagation: Exact Gradients from the Least-Action Principle

arXiv:2602.02281v2 Announce Type: replace-cross Abstract: Backpropagation is typically presented as a symbolic procedure: a backward pass topologically distinct from inference, with non-local error si

local-aiarxiv-cs-ai

A Simulated Federated Analysis of MS-Induced Brain Lesions

arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti

applicationsarxiv-cs-lg

Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs

arXiv:2605.06042v2 Announce Type: replace Abstract: Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scal

agentsarxiv-cs-ro

Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems

arXiv:2605.10555v1 Announce Type: new Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread

Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning

arXiv:2605.08764v1 Announce Type: cross Abstract: Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not mere

researcharxiv-cs-cv