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arXiv:2509.13858v2 Announce Type: replace Abstract: Dataset distillation aims to synthesize a compact dataset from the original large-scale one, enabling highly efficient learning while preserving com

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

DGX agent

arXiv:2502.05157v3 Announce Type: replace Abstract: The perspective of developing trustworthy AI for critical applications in science and engineering requires machine learning techniques that are capa

tutorialsarxiv-cs-lg

14 May 2026

Local Ai

Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices

arXiv:2605.13462v1 Announce Type: new Abstract: Gesture recognition is a cornerstone of Human-Computer Interaction (HCI) for smart eyewear, enabling natural and device-free control in augmented realit

local-aiarxiv-cs-lg

Entropy Aware Reward Guidance for Diffusion Language Model Alignment

arXiv:2602.05000v2 Announce Type: replace-cross Abstract: Reward guidance, also known as posterior sampling, is a popular method for test-time adaptation and post-training in continuous diffusion mode

Exact Verification of Graph Neural Networks with Incremental Constraint Solving

arXiv:2508.09320v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susce

applicationsarxiv-cs-ai

Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm

model-releasesarxiv-cs-cv

arXiv:2605.13402v1 Announce Type: new Abstract: Computing a minimum s-t cut in a graph is a solution to a wide range of computer vision problems, and is often done using the Boykov-Kolmogorov (BK) alg

Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit

arXiv:2409.02708v2 Announce Type: replace Abstract: Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the avai

FOAM: Blocked State Folding for Memory-Efficient LLM Training

arXiv:2512.07112v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However

Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay

model-releasesarxiv-cs-ro

arXiv:2605.13266v1 Announce Type: new Abstract: Many Inertial Navigation Systems (INS) use Global Navigation Satellite System (GNSS) position as the primary measurement to drive filter performance and

Generative Motion In-betweening by Diffusion over Continuous Implicit Representations

arXiv:2605.12778v1 Announce Type: cross Abstract: Recent advances in generative models have yielded impressive progress on motion in-betweening, allowing for more complex, varied, and realistic motion

researcharxiv-cs-cv

Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception

arXiv:2605.13755v1 Announce Type: new Abstract: In recent years, autonomous driving has significantly in creased the demand for high-quality data to train 2D and 3D perception models for safety-critic

safetyarxiv-cs-cv

Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization

arXiv:2605.12782v1 Announce Type: new Abstract: Financial transaction fraud prevention faces challenges such as complex relationship structures, concealed behavioral patterns, and dynamically changing

safetyarxiv-cs-lg

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?

arXiv:2605.12827v1 Announce Type: cross Abstract: Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses t

Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics

arXiv:2605.12728v1 Announce Type: cross Abstract: The power distribution engineering workforce faces a projected shortage of up to 1.5 million engineers by 2030, creating urgent demand for more access

High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation

arXiv:2601.01860v2 Announce Type: replace Abstract: Detecting high-order epistasis is a fundamental challenge in genetic association studies due to the combinatorial explosion of candidate locus combi

(How) Do Large Language Models Understand High-Level Message Sequence Charts?

arXiv:2605.13773v1 Announce Type: cross Abstract: Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether t

Humanwashing -- It Should Leave You Feeling Dirty

arXiv:2605.13723v1 Announce Type: cross Abstract: The phrase 'human in the loop' is increasingly used to imply a sense of safety in relation to AI decision systems. It shouldn't. There are contexts wh

Identification of Non-Transversal Bifurcations of Linkages

arXiv:2605.13094v1 Announce Type: new Abstract: The local analysis is an established approach to the study of singularities and mobility of linkages. Key result of such analyses is a local picture of

researcharxiv-cs-ro

Identifying AI Web Scrapers Using Canary Tokens

arXiv:2605.13706v1 Announce Type: cross Abstract: From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large lan

agentsarxiv-cs-ai

Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization

arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While pre

Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification

arXiv:2601.22853v3 Announce Type: replace Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete

tutorialsarxiv-cs-cv

Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models

arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen

safetyarxiv-cs-cv

'It became a self-fulfilling prophecy': How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

arXiv:2605.13261v1 Announce Type: cross Abstract: In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive p

researcharxiv-cs-ai

Learning Local Constraints for Reinforcement-Learned Content Generators

arXiv:2605.13570v1 Announce Type: new Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually

tutorialsarxiv-cs-ai

Learning to Decide with AI Assistance under Human-Alignment

arXiv:2605.12646v1 Announce Type: cross Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate th

Lifelong Learning in Vision-Language Models: Enhanced EWC with Cross-Modal Knowledge Retention

model-releasesarxiv-cs-ro

arXiv:2605.12789v1 Announce Type: new Abstract: Large language-vision models (LVLMs) such as CLIP, Flamingo, and BLIP have revolutionized AI by enabling understanding across textual and visual modalit

Limits of Personalizing Differential Privacy Budgets

arXiv:2605.13503v1 Announce Type: cross Abstract: A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natur

LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration

arXiv:2605.13782v1 Announce Type: cross Abstract: Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant

agentsarxiv-cs-ai

MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation

arXiv:2605.12640v1 Announce Type: new Abstract: Panoptic segmentation requires the simultaneous recognition of countable thing instances and amorphous stuff regions, placing joint demands on long-rang

researcharxiv-cs-cv

MoCCA: A Movable Circle Probability of Collision Approximation

arXiv:2605.13125v1 Announce Type: new Abstract: In automated driving, crash mitigation is crucial to ensure passenger safety. Accurate avoidance requires precise knowledge of the object's position and

safetyarxiv-cs-ro

Occlusion-Based Object Transportation Around Obstacles With a Swarm of Miniature Robots

arXiv:2605.13006v1 Announce Type: new Abstract: Swarm robotics utilises decentralised self-organising systems to form complex collective behaviours built from the bottom-up using individuals that have

agentsarxiv-cs-ro

On the Sample Complexity of Differentially Private Policy Optimization

arXiv:2510.21060v3 Announce Type: replace-cross Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning robotics, healthcare, and

On the Size Complexity and Decidability of First-Order Progression

arXiv:2605.12691v1 Announce Type: new Abstract: Progression, the task of updating a knowledge base to reflect action effects, generally requires second-order logic. Identifying first-order special cas

researcharxiv-cs-ai

Online Conformal Prediction: Enforcing monotonicity via Online Optimization

arXiv:2605.12668v1 Announce Type: cross Abstract: Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has exte

applicationsarxiv-cs-lg

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks

arXiv:2601.22409v3 Announce Type: replace-cross Abstract: Kolmogorov--Arnold Networks (KANs) have recently emerged as a structured alternative to standard MLPs, yet a principled theory for their train

tutorialsarxiv-cs-ai

PanoWorld: Towards Spatial Supersensing in 360^irc Panorama World

arXiv:2605.13169v1 Announce Type: cross Abstract: Multimodal large laboratory models (MLLMs) still struggle with spatial understanding under the dominant perspective-image paradigm, which inherits the

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction

arXiv:2605.12683v1 Announce Type: cross Abstract: Reconstructing nonlinear dynamical systems (DS) from data (DSR) is a fundamental challenge in science and engineering, but it inherently relies on seq

agentsarxiv-cs-ai

Pattern-Enhanced RT-DETR for Multi-Class Battery Detection

model-releasesarxiv-cs-cv

arXiv:2605.13670v1 Announce Type: new Abstract: Accurate and efficient battery detection is increasingly important for applications in electronic waste recycling, industrial quality control, and autom

Perceptrons and localization of attention's mean-field landscape

arXiv:2601.21366v2 Announce Type: replace Abstract: The forward pass of a Transformer can be seen as an interacting particle system on the unit sphere: time plays the role of layers, particles that of

Posterior Bayesian Neural Networks with Dependent Weights

arXiv:2507.22095v5 Announce Type: replace-cross Abstract: We consider fully connected and feedforward deep neural networks with dependent and possibly heavy-tailed weights, as introduced in [26], to a

Predicting Channel Closures in the Lightning Network with Machine Learning

model-releasesarxiv-cs-lg

arXiv:2605.12759v1 Announce Type: new Abstract: The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

arXiv:2605.12831v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-maki

applicationsarxiv-cs-lg

Re-evaluating Minimum Bayes Risk Decoding for Automatic Speech Recognition

arXiv:2510.19471v2 Announce Type: replace-cross Abstract: Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as ma

Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users

arXiv:2605.12511v1 Announce Type: cross Abstract: Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short spa

applicationsarxiv-cs-lg

Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO

arXiv:2605.12578v1 Announce Type: cross Abstract: The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ab

Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting

model-releasesarxiv-cs-cv

arXiv:2605.13604v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revis

ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding

arXiv:2605.13228v1 Announce Type: cross Abstract: Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and co

Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education

arXiv:2605.12988v1 Announce Type: new Abstract: Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instanc

tutorialsarxiv-cs-ai