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Depth-Aware Rover: A Study of Edge AI and Monocular Vision for Real-World Implementation

arXiv:2604.22331v1 Announce Type: new Abstract: This study analyses simulated and real-world implementations of depth-aware rover navigation, highlighting the transition from stereo vision to monocula

local-aiarxiv-cs-cv

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Human DGX agent

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Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification

DGX agent

arXiv:2603.00177v2 Announce Type: replace-cross Abstract: The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are inc

researcharxiv-cs-lg

27 Apr 2026

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations

arXiv:2604.22629v1 Announce Type: cross Abstract: This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained ac

Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube

model-releasesarxiv-cs-cl

arXiv:2604.22606v1 Announce Type: new Abstract: Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sound

Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts

model-releasesarxiv-cs-cv

arXiv:2604.22515v1 Announce Type: new Abstract: Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity

Differentiable Filtering for Learning Hidden Markov Models

arXiv:2511.10571v2 Announce Type: replace Abstract: Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Class

local-aiarxiv-cs-lg

DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis

model-releasesarxiv-cs-cl

arXiv:2601.23022v3 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world

Dissociating Decodability and Causal Use in Bracket-Sequence Transformers

arXiv:2604.22128v1 Announce Type: new Abstract: When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: i

researcharxiv-cs-cl

Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control

model-releasesarxiv-cs-ai

arXiv:2604.22413v1 Announce Type: cross Abstract: Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while ot

Tutorials

Distilling Vision Transformers for Distortion-Robust Representation Learning

arXiv:2604.22529v1 Announce Type: new Abstract: Self-supervised learning has achieved remarkable success in learning visual representations from clean data, yet remains challenging when clean observat

tutorialsarxiv-cs-cv

DM^3-Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation

arXiv:2604.22014v1 Announce Type: cross Abstract: We present DM^3-Nav, a fully decentralized multi-agent semantic navigation system supporting multimodal open-vocabulary goal specification and multi-o

agentsarxiv-cs-ro

Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement

arXiv:2604.22110v1 Announce Type: new Abstract: Standard supervised classification trains models to imitate the exact labels provided by a perfect oracle. This imitation happens in a single pass, rest

agentsarxiv-cs-lg

DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning

arXiv:2604.22281v1 Announce Type: new Abstract: Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answeri

DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale

arXiv:2604.00813v3 Announce Type: replace-cross Abstract: End-to-end autonomous driving has evolved from the conventional paradigm based on sparse perception into vision-language-action (VLA) models,

agentsarxiv-cs-ai

dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model

arXiv:2604.22152v1 Announce Type: new Abstract: Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for

safetyarxiv-cs-ro

Dynamic Coupling and Indirect Control of Jointed Robots Rolling Atop A Moving Platform

arXiv:2604.22104v1 Announce Type: new Abstract: An asymmetric two-link robot supported atop a flat platform by wheels that roll and pivot freely, but do not slip laterally, will develop forward moment

researcharxiv-cs-ro

Applications

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

arXiv:2604.22325v1 Announce Type: new Abstract: Existing Natural Language Processing (NLP) resources often lack the task-specific information required for real-world problems and provide limited cover

applicationsarxiv-cs-cl

Edit-aware RAW Reconstruction

arXiv:2512.05859v2 Announce Type: replace Abstract: Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater a

Efficient Diffusion Distillation via Embedding Loss

arXiv:2604.22379v1 Announce Type: new Abstract: Recent advances in distilling expensive diffusion models into efficient few-step generators show significant promise. However, these methods typically d

Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search

arXiv:2502.00955v2 Announce Type: replace Abstract: Monte Carlo Tree Search (MCTS) based methods provide promising approaches for generating synthetic data to enhance the self-training of Large Langua

agentsarxiv-cs-cl

EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms

model-releasesarxiv-cs-ai

arXiv:2604.22036v1 Announce Type: cross Abstract: This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part o

Eidolon: A Post-Quantum Signature Scheme Based on k-Colorability in the Age of Graph Neural Networks

arXiv:2602.02689v2 Announce Type: replace-cross Abstract: We propose Eidolon, a post-quantum signature scheme grounded on the NP-complete k-colorability problem. Our construction generalizes the Goldr

Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

arXiv:2604.22119v1 Announce Type: new Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own ob

safetyarxiv-cs-ai

Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests

model-releasesarxiv-cs-ro

arXiv:2604.22189v1 Announce Type: new Abstract: This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing

Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation

arXiv:2603.10093v2 Announce Type: replace-cross Abstract: Recent 3D molecular generation methods primarily use asynchronous auto-regressive or synchronous diffusion models. While auto-regressive model

Estimating Tail Risks in Language Model Output Distributions

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increa

safetyarxiv-cs-ai

Ethics Testing: Proactive Identification of Generative AI System Harms

arXiv:2604.22089v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) h

safetyarxiv-cs-ai

EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges

model-releasesarxiv-cs-cv

arXiv:2604.22595v1 Announce Type: new Abstract: CLIP has demonstrated strong generalization in visual domains through natural language supervision, even for video action recognition. However, most exi

Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations

arXiv:2604.22207v1 Announce Type: cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate

Evaluation of image simulation open source solutions for simulation of synthetic images in lunar environment

arXiv:2604.22296v1 Announce Type: new Abstract: Synthetic image generation is one of the crucial input for planetary missions. It enables researchers and engineers to visualize planned planetary missi

agentsarxiv-cs-cv

EvFlow-GS: Event Enhanced Motion Deblurring with Optical Flow for 3D Gaussian Splatting

arXiv:2604.22183v1 Announce Type: new Abstract: Achieving sharp 3D reconstruction from motion-blurred images alone becomes challenging, motivating recent methods to incorporate event cameras, benefiti

Tutorials

Evidence of an Emergent 'Self' in Continual Robot Learning

arXiv:2603.24350v2 Announce Type: replace-cross Abstract: A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a 'sel

tutorialsarxiv-cs-ai

Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals

arXiv:2604.22539v1 Announce Type: new Abstract: Thematic maps play a central role in academic communication, yet their large-scale design evolution has rarely been examined empirically. This study pre

Expander Hierarchies for Normalized Cuts on Graphs

arXiv:2406.14111v2 Announce Type: replace-cross Abstract: Expander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundament

Explanation of Dynamic Physical Field Predictions using WassersteinGrad: Application to Autoregressive Weather Forecasting

arXiv:2604.22580v1 Announce Type: cross Abstract: As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network pre

Exploiting Low-Rank Structure in Max-K-Cut Problems

arXiv:2602.20376v2 Announce Type: replace-cross Abstract: We approach the Max-3-Cut problem through the lens of maximizing complex-valued quadratic forms and demonstrate that low-rank structure in the

False Feasibility in Variable Impedance MPC for Legged Locomotion

model-releasesarxiv-cs-ro

arXiv:2604.22251v1 Announce Type: new Abstract: Variable impedance model predictive control (MPC) formulations that treat joint stiffness as an instantaneous decision variable operate on a feasible se

Fast, close, non-singular and property-preserving approximations of entropic measures

arXiv:2505.14234v2 Announce Type: replace-cross Abstract: Entropic measures like Shannon entropy (SE), its quantum mechanical analogue von Neumann entropy, and Kullback-Leibler divergence (KL) are key

Fast Neural-Network Approximation of Active Target Search Under Uncertainty

arXiv:2604.22254v1 Announce Type: new Abstract: We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis densi

agentsarxiv-cs-lg

Applications

FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records

arXiv:2604.22534v1 Announce Type: cross Abstract: Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and struc

applicationsarxiv-cs-ai

Federated Nonlinear System Identification

arXiv:2508.15025v5 Announce Type: replace Abstract: We consider federated learning of linearly-parameterized nonlinear systems. We establish theoretical guarantees on the effectiveness of federated no

FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet

arXiv:2604.22494v1 Announce Type: cross Abstract: We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-con

Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation

arXiv:2604.21950v1 Announce Type: cross Abstract: Small language models (1-3B) are practical to run locally, but individually limited on harder code generation tasks. We ask whether composing them int

local-aiarxiv-cs-ai

FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting

model-releasesarxiv-cs-ai

arXiv:2604.22328v1 Announce Type: cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operation. Yet,

FeudalNav: A Simple Framework for Visual Navigation

arXiv:2602.06974v2 Announce Type: replace-cross Abstract: Visual navigation for robotics is inspired by the human ability to navigate environments using visual cues and memory, eliminating the need fo

FILTR: Extracting Topological Features from Pretrained 3D Models

model-releasesarxiv-cs-cv

arXiv:2604.22334v1 Announce Type: new Abstract: Recent advances in pretraining 3D point cloud encoders (e.g., Point-BERT, Point-MAE) have produced powerful models, whose abilities are typically evalua

Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models

arXiv:2604.22166v1 Announce Type: new Abstract: While language models demonstrate sophisticated syntactic capabilities, the extent to which their internal mechanisms align with cross-constructional pr

local-aiarxiv-cs-cl

FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings

arXiv:2604.22176v1 Announce Type: cross Abstract: Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerabil