Search: “applications”

UA-DCM: Uncertainty-aware Causal Decision Making via Effect Bound Decomposition

arXiv:2601.22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having

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Applications

UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models

DGX agent

arXiv:2602.18020v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demons

applicationsarxiv-cs-cv

9 Jun 2026

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision

arXiv:2606.09670v1 Announce Type: cross Abstract: Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these

Wispy to Voluminous: Prior-free Multi-view Capture of Strand-level Facial Hair

arXiv:2606.08041v1 Announce Type: cross Abstract: Facial hair is a defining trait of personal identity, yet remains a critical bottleneck for digital avatars. Recent volumetric methods achieve photore

X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication

arXiv:2606.08437v1 Announce Type: cross Abstract: Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before b

Zero-Flow Encoders

arXiv:2602.00797v3 Announce Type: replace-cross Abstract: Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distr

A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning

arXiv:2504.10102v2 Announce Type: replace Abstract: Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, incr

applicationsarxiv-cs-ro

A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data

arXiv:2606.07128v1 Announce Type: new Abstract: Raw numerical datasets remain less systematically examined in integrity screening than images, plagiarism, or summary-statistic inconsistencies. We deve

A Rolling-Window Framework for Churn Prediction and Behavioral Driver Identification

arXiv:2606.06776v1 Announce Type: new Abstract: Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement

Adversarial Creation and Detection of AI-Generated Social Bot Content

arXiv:2606.07219v1 Announce Type: new Abstract: The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like conten

applicationsarxiv-cs-cl

alpha-PFN: Fast Entropy Search via In-Context Learning

arXiv:2606.07134v1 Announce Type: new Abstract: Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression

arXiv:2606.06567v1 Announce Type: new Abstract: Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately captur

AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation

arXiv:2606.06761v1 Announce Type: cross Abstract: Visuomotor manipulation policies trained via large-scale behavior cloning have achieved strong semantic scene understanding, yet often fail to reliabl

CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection

arXiv:2606.06978v1 Announce Type: new Abstract: Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely

Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems

arXiv:2603.24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversi

Research

Enhancing Conformal Prediction via Class Similarity

arXiv:2511.19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications. Instead of predicting a singl

researcharxiv-cs-lg

Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

arXiv:2606.06800v1 Announce Type: cross Abstract: Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transi

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

Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs

arXiv:2606.07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node class

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606.06864v1 Announce Type: new Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level pre

LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography

arXiv:2606.06901v1 Announce Type: new Abstract: Photography is the art of painting with light, yet nighttime scenes are shaped by competing degradations: intense flares obscure scene structure, while

MalTree: Tracing Malware Evolution from Embeddings at Scale

arXiv:2606.06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary rel

Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

arXiv:2606.06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity

Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis

arXiv:2606.06867v1 Announce Type: new Abstract: Modern medicine relies on heterogeneous data sources spanning radiology, pathology, text reports, and structured clinical information. However, real-wor

Optimal Control Approach for Non-prehensile Ball Juggling Using a 7-DoF Manipulator

arXiv:2606.06704v1 Announce Type: new Abstract: Non-prehensile object manipulation skills are important for real-world robot interactions, enabling highly dynamic tasks such as balancing a glass on a

applicationsarxiv-cs-ro

Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges

arXiv:2510.03381v3 Announce Type: replace-cross Abstract: Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic

Sparsely gated tiny linear experts

arXiv:2606.07414v1 Announce Type: new Abstract: Sparsity allows scaling model parameters without proportionally increasing computational cost. While mixture of experts (MoE) models are made increasing

SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models

arXiv:2606.06907v1 Announce Type: cross Abstract: Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data. However, the scarcity of high-quali

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

TargetSEC: Plug-and-Play In-the-Wild Speech Emotion Conversion via Arousal-Conditioned Latent Style Diffusion

arXiv:2606.07293v1 Announce Type: cross Abstract: Speech Emotion Conversion (SEC) aims to transform the emotion of a source utterance into a target emotion while preserving content and speaker identit

Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

arXiv:2606.07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of st

USU-Corn-WeedDB: A UAV RGB Image Dataset for Multi-Species Weed Detection in Forage Corn

arXiv:2606.06709v1 Announce Type: new Abstract: Weed pressure in forage corn production causes yield losses of up to 31.5%, yet site-specific weed management (SSWM) systems built on UAV imagery and de

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

arXiv:2403.00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the perf

Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation

arXiv:2606.06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependen

CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

arXiv:2606.05413v1 Announce Type: cross Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-dri

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

arXiv:2606.05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased ex

From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability

arXiv:2606.05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder. These Breach-and-At

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606.06328v1 Announce Type: cross Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because elect

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

arXiv:2509.20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with ex

Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study

arXiv:2606.06375v1 Announce Type: new Abstract: Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data. This work exploits th

Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory

arXiv:2606.05334v1 Announce Type: new Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cann

ActiveMimic: Egocentric Video Pretraining with Active Perception

arXiv:2606.06194v1 Announce Type: cross Abstract: Egocentric human video offers a scalable alternative to robot data for pretraining, yet models pretrained on such video consistently underperform thos

Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue

arXiv:2509.15061v2 Announce Type: cross Abstract: The ultimate goal of embodied agents is to create collaborators that can interact with humans, not mere executors that passively follow instructions.

Bootstrapping Semantic Layer from Execution for Text-to-SQL

arXiv:2606.05634v1 Announce Type: new Abstract: Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this b

applicationsarxiv-cs-cl

Dual Feature Decoupling for Fine-Grained OOD Detection

arXiv:2606.05536v1 Announce Type: new Abstract: Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detec

EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading

arXiv:2606.06350v1 Announce Type: new Abstract: Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student a

applicationsarxiv-cs-cl

Efficient Mean Curvature Computation on High-Dimensional Data Manifolds

arXiv:2606.06329v1 Announce Type: cross Abstract: Estimating local mean curvature at each point of a high-dimensional dataset is a key ingredient of geometry-aware machine learning algorithms, such as