Search: “applications”

StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling

arXiv:2507.05240v2 Announce Type: replace-cross Abstract: Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low

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Applications

The complexities of patient-centred conversational artificial intelligence

DGX agent

arXiv:2607.08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment. However, chatbot development and e

applicationsarxiv-cs-ai

10 Jul 2026

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs

arXiv:2607.08734v1 Announce Type: new Abstract: Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively

Unpaired Joint Distribution Modeling via Multi-Scale Image Representations

arXiv:2607.08198v1 Announce Type: new Abstract: This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasib

Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis

arXiv:2607.08748v1 Announce Type: new Abstract: In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education. Based on objecti

Local Ai

Activation Quantization of Vision Encoders Needs Prefixing Registers

arXiv:2510.04547v5 Announce Type: replace-cross Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-lang

local-aiarxiv-cs-cv

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

arXiv:2603.11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, like

Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data

arXiv:2607.07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The margi

Compositional Motion Generation from Demonstration with Object-Centric Neural Fields

arXiv:2607.07129v1 Announce Type: new Abstract: Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Levera

applicationsarxiv-cs-ro

Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?

arXiv:2607.06632v1 Announce Type: new Abstract: Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy syste

Dynamic-in-Few-Step: Unifying Dynamic Computation and Few-Step Distillation for Efficient Video Generation

arXiv:2607.06631v1 Announce Type: cross Abstract: Video Diffusion Models (VDMs) have demonstrated superior generation quality but suffer from prohibitive computational costs. While recent few-step dis

Dynamic Object Detection and Tracking in Construction: A Fisheye Camera and LiDAR Sensor Fusion Model

arXiv:2607.06896v1 Announce Type: cross Abstract: Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environme

Face-trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators

arXiv:2607.07545v1 Announce Type: new Abstract: Recent advances in generative Artificial Intelligence have made synthetic face images increasingly realistic, creating new challenges for multimedia for

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning

arXiv:2512.02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions. Extracting

From Jumps to Signatures: a Generative Method for Temporal Point Processes

arXiv:2607.06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantee

G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening

arXiv:2603.14412v2 Announce Type: replace Abstract: Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution m

GeoGS-SLAM: Geometry-Only Gaussian Splatting for Dense Monocular SLAM

arXiv:2607.07452v1 Announce Type: new Abstract: Dense visual SLAM is a fundamental problem in robotics. Recent advances in 3DGS have demonstrated its potential for dense SLAM. Existing 3DGS frameworks

applicationsarxiv-cs-ro

Inertia-1: An Open Exploration of Wearable Motion Foundation Models

arXiv:2607.06617v1 Announce Type: cross Abstract: Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet i

L-GTA: Latent Generative Modeling for Time Series Augmentation

arXiv:2507.23615v2 Announce Type: replace-cross Abstract: Data augmentation is becoming increasingly important across various areas of time series analysis, including forecasting, classification, and

Hardware

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607.06922v1 Announce Type: cross Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding t

hardwarearxiv-cs-cv

Safety

LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection

arXiv:2607.06592v1 Announce Type: cross Abstract: Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial

safetyarxiv-cs-ai

Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting

arXiv:2607.07016v1 Announce Type: cross Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mo

Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset

arXiv:2603.02150v2 Announce Type: replace-cross Abstract: The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this inf

Physical activities enable scalable foundation modelling for broad-spectrum health prediction

arXiv:2607.06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific

PriGo: Test-Time Primitive Guidance to Diffusion and Flow Policies for Adaptive Robotic Manipulation

arXiv:2607.07076v1 Announce Type: new Abstract: Imitation learning has enabled remarkable progress in robotic manipulation, especially with diffusion and flow-based policies that generate complex visu

applicationsarxiv-cs-ro

Quantum simulation of real-world nonlinear dynamics via Koopman method

arXiv:2607.07338v1 Announce Type: cross Abstract: Nonlinear dynamics is ubiquitous in nature, ranging from chemical pattern formation to ocean circulation, yet its simulation on quantum computers is f

Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26

arXiv:2607.07498v1 Announce Type: cross Abstract: Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a d

Robust Human-AI Complementarity under Uncertainty

arXiv:2607.06656v1 Announce Type: new Abstract: Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human

SoccerNet 2026 Challenges Results

arXiv:2607.07320v1 Announce Type: new Abstract: The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision rese

Stability of Flow Models for Graph Signals

arXiv:2607.07510v1 Announce Type: cross Abstract: Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While f

tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series

arXiv:2607.06690v1 Announce Type: cross Abstract: Finance, sensing, and demand streams violate the exchangeability that IID conformal prediction and the IID bootstrap assume, and existing libraries im

A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid

arXiv:2602.11391v4 Announce Type: replace Abstract: Objective: This study develops and validates a patient simulation framework that aligns with the National Institute of Standards and Technology (NIS

applicationsarxiv-cs-cl

Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection

arXiv:2607.05434v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behavi

Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines

arXiv:2607.05680v1 Announce Type: cross Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when t

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

arXiv:2607.05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's i

Data-dependent Evaluations for Budgeted Submodular Maximization

arXiv:2607.05759v1 Announce Type: cross Abstract: Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607.05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine

EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion

arXiv:2607.06217v1 Announce Type: new Abstract: Enhancing videos under extreme low-light conditions remains challenging due to the difficulty of balancing restoration quality and computational efficie

FedDAF: Federated Domain Adaptation Using Model Functional Distance

arXiv:2509.11819v2 Announce Type: replace-cross Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating

Full-range Binary Classifier Calibration for Stable Model Updates in Production

arXiv:2607.05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparative

Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction

arXiv:2607.05449v1 Announce Type: cross Abstract: Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for i

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM

arXiv:2607.05493v1 Announce Type: new Abstract: Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to

HERB: Human-augmented Efficient Reinforcement learning for Bin-packing

arXiv:2504.16595v2 Announce Type: replace-cross Abstract: Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics. When dealing with highly diverse 3D obj

Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator

arXiv:2607.05765v1 Announce Type: new Abstract: Embodied navigation aims to build agents that interpret multimodal goals, reason in 3D space, and reach target destinations reliably in the real world.

Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH

arXiv:2607.05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, r

Safety

IUU+DB: Tracking Illegal, Unreported, and Unregulated Fishing, Seafood Fraud, and Labor Abuse through LLM-driven Information Extraction

arXiv:2606.18181v2 Announce Type: replace-cross Abstract: Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas t

safetyarxiv-cs-ai

Learning 4D Geometric Priors for Inference-Efficient World Action Models

arXiv:2607.05468v1 Announce Type: cross Abstract: World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action seque

Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation

arXiv:2607.06564v1 Announce Type: cross Abstract: Recently, Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse tasks. However, effective robotic manipulation in