Search: “applications”

Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning

arXiv:2604.19823v1 Announce Type: cross Abstract: Rabies remains a major public health concern across many African and Asian countries, where accurate diagnosis is critical for effective epidemiologic

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Agents

Real-Time Initialization of Unknown Anchors for UWB-aided Navigation

DGX agent

arXiv:2506.15518v2 Announce Type: replace Abstract: This paper presents a framework for the real-time initialization of unknown Ultra-Wideband (UWB) anchors in UWB-aided navigation systems. The method

agentsarxiv-cs-ro

23 Apr 2026

Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

arXiv:2604.19838v1 Announce Type: new Abstract: Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While

safetyarxiv-cs-ai

REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer

model-releasesarxiv-cs-cv

arXiv:2601.08558v2 Announce Type: replace Abstract: Incomplete point clouds captured by 3D sensors often result in the loss of both geometric and semantic information. Most existing point cloud comple

Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids

arXiv:2604.20403v1 Announce Type: new Abstract: Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observabili

Rodrigues Network for Learning Robot Actions

arXiv:2506.02618v2 Announce Type: replace-cross Abstract: Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers l

safetyarxiv-cs-cv

Local Ai

SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition

arXiv:2604.20146v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to extract named entities and localize their visual regions within image-text pairs, serving

local-aiarxiv-cs-cl

Sampling-Aware Quantization for Diffusion Models

arXiv:2505.02242v2 Announce Type: replace Abstract: Diffusion models have recently emerged as the dominant approach in visual generation tasks. However, the lengthy denoising chains and the computatio

safetyarxiv-cs-cv

scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics

arXiv:2604.20003v1 Announce Type: cross Abstract: The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we i

Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation

arXiv:2604.19777v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit a well-documented positional bias when processing long input contexts: information in the middle of a context win

Semantic-Fast-SAM: Efficient Semantic Segmenter

arXiv:2604.20169v1 Announce Type: new Abstract: We propose Semantic-Fast-SAM (SFS), a semantic segmentation framework that combines the Fast Segment Anything model with a semantic labeling pipeline to

Agents

Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings

arXiv:2604.20436v1 Announce Type: cross Abstract: Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation. While vibe codi

agentsarxiv-cs-ai

Storm Surge Modeling, Bias Correction, Graph Neural Networks, Graph Convolution Networks

arXiv:2604.20688v1 Announce Type: cross Abstract: Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent tren

safetyarxiv-cs-ai

Supplement Generation Training for Enhancing Agentic Task Performance

arXiv:2604.20727v1 Announce Type: cross Abstract: Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid o

Agents

The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms

arXiv:2604.19803v1 Announce Type: new Abstract: Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper,

agentsarxiv-cs-ai

Tutorials

The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World

arXiv:2407.17395v5 Announce Type: replace Abstract: Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distr

tutorialsarxiv-cs-lg

The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks

arXiv:2504.19239v2 Announce Type: replace-cross Abstract: Quantum neural networks hold promise for tackling computationally challenging tasks that are intractable for classical computers. However, the

The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agents

arXiv:2511.03690v2 Announce Type: replace-cross Abstract: Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task

Throat and acoustic paired speech dataset for deep learning-based speech enhancement

arXiv:2502.11478v3 Announce Type: replace-cross Abstract: In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a

safetyarxiv-cs-lg

Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification

arXiv:2604.20231v1 Announce Type: new Abstract: Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase

safetyarxiv-cs-ro

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents

model-releasesarxiv-cs-cl

arXiv:2601.20144v3 Announce Type: replace Abstract: Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized se

Transparent Screening for LLM Inference and Training Impacts

arXiv:2604.19757v1 Announce Type: cross Abstract: This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited obs

UCCL-Zip: Lossless Compression Supercharged GPU Communication

arXiv:2604.17172v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) has made GPU communication a critical bottleneck. While prior work reduces communication volu

WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2604.20398v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic mul

Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images

arXiv:2604.20213v1 Announce Type: new Abstract: Accurate segmentation of maxillary sinus in panoramic X-ray images is essential for dental diagnosis and surgical planning; however, this task remains r

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

arXiv:2507.08540v5 Announce Type: replace-cross Abstract: The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection method

A neural operator framework for data-driven discovery of stability and receptivity in physical systems

arXiv:2604.19465v1 Announce Type: cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fu

AI-Based Detection of Temporal Changes in MR-Linac Images Acquired During Routine Prostate Radiotherapy

arXiv:2602.04983v2 Announce Type: replace-cross Abstract: Purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy an

An Experimental Characterization of Mechanical Layer Jamming Systems

model-releasesarxiv-cs-ro

arXiv:2511.07882v2 Announce Type: replace Abstract: Organisms in nature, such as Cephalopods and Pachyderms, exploit stiffness modulation to achieve amazing dexterity in the control of their appendage

Article and Comment Frames Shape the Quality of Online Comments

arXiv:2603.27889v2 Announce Type: replace Abstract: Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. Wh

researcharxiv-cs-cl

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation

arXiv:2507.06321v2 Announce Type: replace Abstract: Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science,

Agents

CityRAG: Stepping Into a City via Spatially-Grounded Video Generation

arXiv:2604.19741v1 Announce Type: new Abstract: We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video

agentsarxiv-cs-cv

Local Ai

COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition

arXiv:2503.07259v2 Announce Type: replace-cross Abstract: The goal of creating intelligent, human-centered wearable systems for continuous activity understanding faces a fundamental trade-off: Egocent

local-aiarxiv-cs-ai

DADA: Dual Averaging with Distance Adaptation

arXiv:2501.10258v2 Announce Type: replace-cross Abstract: We present a novel universal gradient method for solving convex optimization problems. Our algorithm, Dual Averaging with Distance Adaptation

Tutorials

Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification

arXiv:2604.19658v1 Announce Type: new Abstract: Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-dama

tutorialsarxiv-cs-lg

Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

arXiv:2603.04445v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mod

researcharxiv-cs-cl

Efficient Autoregressive Inference for Transformer Probabilistic Models

arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling

model-releasesarxiv-cs-lg

arXiv:2509.03726v2 Announce Type: replace-cross Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions mu_{ext{target}}(x) propto exp(-E(x)/T), is fundamental to many

Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation

arXiv:2602.11631v2 Announce Type: replace-cross Abstract: Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but ar

EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training

arXiv:2411.16312v2 Announce Type: replace Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth l

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge

arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition

arXiv:2604.19357v1 Announce Type: new Abstract: The evaluation of machine learning models typically relies mainly on performance metrics based on loss functions, which risk to overlook changes in perf

safetyarxiv-cs-lg

FedSEA: Achieving Benefit of Parallelization in Federated Online Learning

arXiv:2604.19336v1 Announce Type: new Abstract: Online federated learning (OFL) has emerged as a popular framework for decentralized decision-making over continuous data streams without compromising c

Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition

arXiv:2604.03476v2 Announce Type: replace-cross Abstract: Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable f

Framelet-Based Blind Image Restoration with Minimax Concave Regularization

arXiv:2604.19314v1 Announce Type: new Abstract: Recovering corrupted images is one of the most challenging problems in image processing. Among various restoration tasks, blind image deblurring has bee

safetyarxiv-cs-cv

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS

arXiv:2604.18873v1 Announce Type: new Abstract: Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure,

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines

arXiv:2508.06226v2 Announce Type: replace Abstract: Geometry problem solving (GPS) poses significant challenges for Multimodal Large Language Models (MLLMs) in diagram comprehension, knowledge applica

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation

arXiv:2410.16893v3 Announce Type: replace-cross Abstract: Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non