Search: “papers”

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

arXiv:2606.23210v1 Announce Type: new Abstract: Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw

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Local Ai

Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference

DGX agent

arXiv:2504.08061v2 Announce Type: replace Abstract: Traffic prediction is difficult due to the complex interplay of temporal evolution, spatial interactions, and delayed spatio-temporal propagation ov

local-aiarxiv-cs-cv

23 Jun 2026

ELDiff: When Evidential Learning Meets Text-to-Image Diffusion

model-releasesarxiv-cs-cv

arXiv:2606.20924v1 Announce Type: new Abstract: In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image s

Local Ai

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 Road Safety: An IoT-Based Accident Detection and Prevention Mechanism

arXiv:2606.22381v1 Announce Type: cross Abstract: Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These inc

Essential Subspace Merging for Multi-Task Learning

arXiv:2606.19164v2 Announce Type: replace Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint

Estimating Learners' Skill Acquisition Without Temporal Information

arXiv:2606.20611v1 Announce Type: cross Abstract: Recent research in educational data mining, especially knowledge tracing, has focused on predicting learners' future knowledge states to support adapt

Expected Free Energy-based Planning as Variational Inference

arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance

arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr

Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus

Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception

arXiv:2606.21422v1 Announce Type: new Abstract: The rapid proliferation of Internet of Medical Things (IoMT) devices introduces critical cybersecurity vulnerabilities in healthcare environments where

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

FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation

arXiv:2606.23370v1 Announce Type: cross Abstract: Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts.

FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals

arXiv:2606.23091v1 Announce Type: new Abstract: Wireless sensor network (WSNs) stands out as a burgeoning and promising domain in intelligent sensing. Owing to various factors such as sudden sensor ma

Local Ai

Flow6D: Discrete-to-Continuous Flow Matching for Efficient and Accurate Category-Level 6D Pose Estimation

arXiv:2606.23293v1 Announce Type: new Abstract: 6D pose estimation is a key task in computer vision and embodied AI, widely used in robotic manipulation, augmented reality, etc. Existing methods direc

local-aiarxiv-cs-cv

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models

arXiv:2606.23087v1 Announce Type: new Abstract: Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either

ForeHOI: Feed-forward 3D Object Reconstruction from Daily Hand-Object Interaction Videos

arXiv:2602.06226v2 Announce Type: replace Abstract: The ubiquity of monocular videos capturing daily hand-object interactions presents a valuable resource for embodied intelligence. While 3D hand reco

Frequency-Domain Neural ODEs for Modeling Non-Linear Dynamical Systems

arXiv:2606.22075v1 Announce Type: new Abstract: Standard continuous-depth models, such as Neural Ordinary Differential Equations (NODEs), offer significant advantages in modeling physical systems by l

From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints

arXiv:2606.22664v1 Announce Type: cross Abstract: Consumer financial complaints provide a valuable source of information for identifying service failures, dispute frictions, and operational deficienci

From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation

model-releasesarxiv-cs-cv

arXiv:2606.22168v1 Announce Type: new Abstract: Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used f

From Driving Videos to Simulatable Scenarios

arXiv:2606.21993v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) face driving scenarios ranging from routine traffic to rare events. To assess safety it is crucial to reproduce these scenar

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks

arXiv:2606.22258v1 Announce Type: new Abstract: Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has signif

From Markov to Laplace: How Mamba In-Context Learns Markov Chains

arXiv:2502.10178v2 Announce Type: replace Abstract: While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viabl

Tutorials

From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information

arXiv:2606.20842v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-qua

tutorialsarxiv-cs-cv

Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs

arXiv:2606.21440v1 Announce Type: new Abstract: Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This o

GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs

arXiv:2606.23299v1 Announce Type: new Abstract: Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptim

H-RINS: Hierarchical Tightly-coupled Radar-Inertial State Estimation via Smoothing and Mapping

model-releasesarxiv-cs-ro

arXiv:2603.14109v2 Announce Type: replace Abstract: Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: sparse body

Hierarchical Pooling for Sheaf Neural Networks

arXiv:2606.20932v1 Announce Type: new Abstract: Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

arXiv:2508.05212v2 Announce Type: replace-cross Abstract: With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogene

Horizon Adaptive Offline Policy Learning via Value Stitching

arXiv:2606.21136v1 Announce Type: new Abstract: Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional tempor

Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks

model-releasesarxiv-cs-cv

arXiv:2606.22935v1 Announce Type: new Abstract: Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these d

In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts

arXiv:2603.25857v2 Announce Type: replace Abstract: The capabilities of large language models (LLMs) have expanded beyond natural language processing to scientific prediction tasks, including molecula

Integrated cloud-based architecture for robot-robot and human-robot collaboration using ROS 2--MQTT in Mediterranean Greenhouses

arXiv:2606.22682v1 Announce Type: new Abstract: The imperative to develop more sustainable agriculture demands a transition from isolated automation toward the deployment of multi-robot systems (MRS)

applicationsarxiv-cs-ro

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

arXiv:2606.23177v1 Announce Type: new Abstract: Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While pr

Inverting the Bellman Equation: From Q-Values to World Models

arXiv:2606.21173v1 Announce Type: new Abstract: Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel P

Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs

arXiv:2606.20961v1 Announce Type: new Abstract: Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method

ISOPoT: Imaging Sonar Odometry by Point Tracking

arXiv:2606.23006v1 Announce Type: new Abstract: Reliable navigation in underwater environments remains a key challenge in marine robotics. In such scenarios, forward-looking sonars are a natural choic

applicationsarxiv-cs-ro

Iterative Convex Optimization with Control Barrier Functions for Obstacle Avoidance among Polytopes

arXiv:2603.05916v2 Announce Type: replace Abstract: Obstacle avoidance of polytopic obstacles by polytopic robots is a challenging problem in optimization-based control and trajectory planning. Many e

safetyarxiv-cs-ro

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

arXiv:2606.22892v1 Announce Type: cross Abstract: The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'ex

Jacobian-Adaptive Weighting for Stability: Enhancing Long-term Rollout of Neural Partial Differential Equation Solvers via Spatially-Adaptive Regularization

arXiv:2603.05538v3 Announce Type: replace Abstract: Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors d

Local Ai

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

arXiv:2509.22307v2 Announce Type: replace Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental extit{``efficiency / robustness conflict''}, particularly when proces

local-aiarxiv-cs-cv

JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems

arXiv:2606.20686v1 Announce Type: new Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micr

safetyarxiv-cs-ro

Kernel of Partition Paths: A Unified Representation for Tree Ensembles

arXiv:2606.18853v2 Announce Type: replace-cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening rou

Learning Adaptive Dynamical Features via Multi-au Liquid-Mamba for All-in-one Image Restoration

arXiv:2606.22801v1 Announce Type: new Abstract: Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong p

Learning Entropy Signature for Image Representation and Classification

arXiv:2606.22634v1 Announce Type: new Abstract: Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distribut

Learning Graphs through Continuous Information Entropy Fields

arXiv:2606.22895v1 Announce Type: new Abstract: Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This

Learning Stable Canonical Worlds for Novel View Synthesis and Beyond

arXiv:2606.23027v1 Announce Type: new Abstract: Feed-forward Gaussian splatting (FFGS) facilitates real-time novel view synthesis, yet current methods often remain tied to view-dependent predictions.

LK Jam: System Architecture and Implementation of a Real-Time Human-AI Interactive Music Generation System using Role-Aware GRU

arXiv:2606.21018v1 Announce Type: cross Abstract: As artificial intelligence advances into the era of Embodied AI, live musical interaction urgently needs to break free from the limitations of offline