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arXiv:2605.13075v1 Announce Type: cross Abstract: Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of

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SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring

DGX agent

arXiv:2605.12916v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing sp

agentsarxiv-cs-lg

14 May 2026

SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning

model-releasesarxiv-cs-lg

arXiv:2605.12872v1 Announce Type: new Abstract: Despite the recent success of Multimodal Foundation Models (FMs), their reliance on massive paired datasets limits their applicability in low-data and r

SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

arXiv:2602.07342v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supp

Tutorials

VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

tutorialsarxiv-cs-ai

3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark

model-releasesarxiv-cs-cv

arXiv:2605.12437v1 Announce Type: new Abstract: Retrospective novel view synthesis (NVS) of dynamic scenes is fundamental to applications such as sports. Recent dynamic 3D Gaussian Splatting (3DGS) ap

A New Technique for AI Explainability using Feature Association Map

model-releasesarxiv-cs-lg

arXiv:2605.12350v1 Announce Type: new Abstract: Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI sys

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

model-releasesarxiv-cs-cl

arXiv:2412.05225v3 Announce Type: replace Abstract: Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and proce

Beyond Point Estimates: Distributional Uncertainty in Machine Learning Performance Evaluation

arXiv:2501.16931v2 Announce Type: replace Abstract: Machine learning models are often evaluated using point estimates of performance metrics such as accuracy, F1 score, or mean squared error. Such sum

Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos

arXiv:2605.12320v1 Announce Type: new Abstract: Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to auto

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)

arXiv:2510.06719v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application i

researcharxiv-cs-cl

EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records

arXiv:2605.12335v1 Announce Type: cross Abstract: Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effect

safetyarxiv-cs-lg

EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models

arXiv:2605.11859v1 Announce Type: new Abstract: Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great p

safetyarxiv-cs-ro

How far can bias go? Tracing bias from pretraining data to alignment

arXiv:2411.19240v2 Announce Type: replace Abstract: As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much wo

safetyarxiv-cs-cl

Information theoretic underpinning of self-supervised learning by clustering

arXiv:2605.11870v1 Announce Type: new Abstract: Self-supervised learning (SSL) is recognized as an essential tool for building foundation models for Artificial Intelligence applications. The advances

Agents

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators

arXiv:2210.09114v2 Announce Type: replace Abstract: For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic enviro

agentsarxiv-cs-ro

Learning U-Statistics with Active Inference

arXiv:2605.11638v1 Announce Type: cross Abstract: U-statistics play a central role in statistical inference. In many modern applications, however, acquiring the labels required for U-statistics is cos

Local Ai

Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics

arXiv:2412.18594v3 Announce Type: replace Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamic

local-aiarxiv-cs-lg

Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making

arXiv:2602.07668v2 Announce Type: replace Abstract: The looking-in-looking-out (LILO) framework has enabled intelligent vehicle applications that understand both the outside scene and the driver state

Local Ai

LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows

arXiv:2605.11368v1 Announce Type: new Abstract: We study the application of recent Edit Flows for inference-time reward control for DNA sequence generation. Unlike most reward-guided DNA generation fr

local-aiarxiv-cs-lg

Manifold Sampling via Entropy Maximization

arXiv:2605.12338v1 Announce Type: new Abstract: Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes conver

Metaphor Is Not All Attention Needs

arXiv:2605.12128v1 Announce Type: new Abstract: Large language models are increasingly deployed in safety-critical applications, where their ability to resist harmful instructions is essential. Althou

safetyarxiv-cs-cl

More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting

arXiv:2605.11402v1 Announce Type: new Abstract: Deep learning-based website fingerprinting has emerged as an effective technique for inferring the websites users visit. Although existing methods achie

safetyarxiv-cs-lg

OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation

arXiv:2605.12480v1 Announce Type: new Abstract: Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal align

Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection

arXiv:2603.23677v2 Announce Type: replace Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to e

Local Ai

Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs

arXiv:2605.11128v1 Announce Type: new Abstract: Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a n

local-aiarxiv-cs-cl

See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models

arXiv:2510.05408v2 Announce Type: replace Abstract: Recovering the past from present observations is an intriguing challenge with potential applications in forensics and scene analysis. Thermal imagin

The Midas Touch for Metric Depth

arXiv:2605.11578v1 Announce Type: new Abstract: Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by t

VNDUQE: Information-Theoretic Novelty Detection using Deep Variational Information Bottleneck

arXiv:2605.11551v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) samples is critical for safe deployment of neural networks in safety-critical applications. While maximum softmax

An Annotation Scheme and Classifier for Personal Facts in Dialogue

model-releasesarxiv-cs-cl

arXiv:2605.10339v1 Announce Type: new Abstract: The advancement of Large Language Models (LLMs) has enabled their application in personalized dialogue systems. We present an extended annotation scheme

Artificial Intelligence in Number Theory: LLMs for Algorithm Generation and Ensemble Methods for Conjecture Verification

arXiv:2504.19451v3 Announce Type: cross Abstract: This paper presents two concrete applications of Artificial Intelligence to algorithmic and analytic number theory. Recent benchmarks of large languag

BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition

model-releasesarxiv-cs-cv

arXiv:2605.08296v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports broad healthcare and behavior science applications. However, data heterogeneity and the

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

arXiv:2605.08988v1 Announce Type: cross Abstract: Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular

Agents

Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems

arXiv:2605.08994v1 Announce Type: cross Abstract: The current catalyst discovery and development pipeline for energy-intensive applications like methane conversion remains bottlenecked by expensive tr

agentsarxiv-cs-lg

Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation

arXiv:2502.08943v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated significant utility in real-world applications, exhibiting impressive capabilities in natural l

Bridging Sequence and Graph Structure for Epigenetic Age Prediction

arXiv:2605.10541v1 Announce Type: new Abstract: Epigenetic clocks based on DNA methylation have emerged as powerful tools for estimating biological age, with broad applications in aging research, age-

researcharxiv-cs-ai

Challenges and opportunities for AI to help deliver fusion energy

arXiv:2603.25777v2 Announce Type: replace-cross Abstract: There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. Ho

researcharxiv-cs-ai

Constructive conditional normalizing flows

arXiv:2602.08606v2 Announce Type: replace-cross Abstract: Motivated by applications in conditional sampling, given a probability measure mu and a diffeomorphism phi, we consider the problem of simulta

Dependency-Aware Discrete Diffusion for Scene Graph Generation

arXiv:2605.09065v1 Announce Type: new Abstract: Scene graphs (SGs) represent objects and their relationships as structured graphs, enabling applications in image generation, robotics, and 3D understan

Tutorials

diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories

arXiv:2605.10647v1 Announce Type: new Abstract: Trajectories are nowadays valuable information for a wide range of applications. However they are also inherently sensitive, as they contain highly pers

tutorialsarxiv-cs-ai

Agents

Distill, Diffuse, and Semanticize (DDS): Annotation-Free 3D Scene Understanding Based on Multi-Granularity Distillation and Graph-Diffusion-Based Segmentation

arXiv:2605.08293v1 Announce Type: new Abstract: 3D semantic scene understanding has broad applications in digital twins, autonomous driving, smart agriculture, and embodied perception. However, dense

agentsarxiv-cs-cv

Distributional Reinforcement Learning via the Cramer Distance

arXiv:2605.08104v1 Announce Type: new Abstract: This paper explores the application of the Soft Actor-Critic (SAC) algorithm within a Distributional Reinforcement Learning setting and introduces an im

Egocentric Whole-Body Human Mesh Recovery with Prior-Guided Learning

arXiv:2605.08606v1 Announce Type: new Abstract: Egocentric human mesh recovery (HMR) from monocular head-mounted cameras is increasingly important for AR/VR applications, but remains challenging due t

Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling

arXiv:2605.08377v1 Announce Type: new Abstract: In many practical applications it is important to build symmetries into neural network architectures. Consider the important case of permutation symmetr

EMO: Pretraining Mixture of Experts for Emergent Modularity

arXiv:2605.06663v2 Announce Type: replace Abstract: Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of cap

researcharxiv-cs-cl

Explaining Graph Neural Networks for Node Similarity on Graphs

arXiv:2407.07639v2 Announce Type: replace-cross Abstract: Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks

researcharxiv-cs-ai

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

arXiv:2605.09428v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate fr

From Pixels to Concepts: Do Segmentation Models Understand What They Segment?

model-releasesarxiv-cs-cv

arXiv:2605.09591v1 Announce Type: new Abstract: Segmentation is a fundamental vision task underlying numerous downstream applications. Recent promptable segmentation models, such as Segment Anything M