Search: “applications”

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de

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Applications

MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders

DGX agent

arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision

applicationsarxiv-cs-ai

25 May 2026

PLACE: Prompt Learning for Attributed Community Search in Large Graphs

arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enli

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

arXiv:2605.23241v1 Announce Type: new Abstract: Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) me

Security of LLM-generated Code: A Comparative Analysis

arXiv:2605.23091v1 Announce Type: cross Abstract: The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons in

Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping

arXiv:2605.23160v1 Announce Type: cross Abstract: We present Semantic-Aware Guided Exploration, SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage

Sparser Block-Sparse Attention via Token Permutation

arXiv:2510.21270v2 Announce Type: replace-cross Abstract: Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems pr

Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts

arXiv:2605.23597v1 Announce Type: new Abstract: Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex env

applicationsarxiv-cs-cl

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting

arXiv:2509.15105v3 Announce Type: replace Abstract: Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across divers

Safety

Task-Awareness Improves LLM Generations and Uncertainty

arXiv:2601.21500v2 Announce Type: replace Abstract: In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values,

safetyarxiv-cs-lg

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems

arXiv:2605.23024v1 Announce Type: new Abstract: Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Fre

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics

arXiv:2605.22968v1 Announce Type: cross Abstract: Machine learning methods provide a methodological innovation that can help screen for cardiovascular disease through noninvasive and readily available

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes

arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalabili

Model Releases

Vector Retrieval with Similarity and Diversity: How Hard Is It?

model-releasesarxiv-cs-cl

arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search

When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance

arXiv:2605.22975v1 Announce Type: new Abstract: We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypotheti

applicationsarxiv-cs-cl

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting

arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conf

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction

arXiv:2605.21528v1 Announce Type: new Abstract: Accurate and reproducible disease risk prediction remains challenging due to heterogeneous features, limited samples, and severe class imbalance. This s

Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations

arXiv:2605.22275v1 Announce Type: new Abstract: Kernel methods are typically formulated under the assumption of exact, noise-free access to the Gram matrix. However, in emerging settings such as quant

Model Releases

Characterizing the Fault Response of the Intel Neural Compute Stick 2 Under Single-Pulse Electromagnetic Fault Injection

model-releasesarxiv-cs-lg

arXiv:2605.22437v1 Announce Type: cross Abstract: Vision processing units and other commercial neural-network inference accelerators are increasingly deployed in safety-relevant edge applications, but

Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series

arXiv:2605.21542v1 Announce Type: new Abstract: Country-level temporal panels are widely used in empirical analysis. Researchers often need to audit how different entities respond to historical signal

Event-Aware Prompt Learning for Dynamic Graphs

arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph le

FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection

arXiv:2605.22779v1 Announce Type: cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines r

Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs

arXiv:2602.18141v2 Announce Type: replace Abstract: Long-range graph tasks are challenging for Graph Neural Networks (GNNs): global mechanisms such as attention or rewiring schemes can be computationa

Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions

arXiv:2605.22612v1 Announce Type: cross Abstract: Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation-

LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation

arXiv:2605.22054v1 Announce Type: new Abstract: The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayes

Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions

arXiv:2605.21548v1 Announce Type: cross Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal st

No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation

arXiv:2605.22248v1 Announce Type: new Abstract: Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most p

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction

arXiv:2605.22338v1 Announce Type: new Abstract: Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states th

Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier

arXiv:2605.22746v1 Announce Type: new Abstract: Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (E

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy

arXiv:2605.21780v1 Announce Type: new Abstract: Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training and

Safety

Proxy-Based Approximation of Shapley and Banzhaf Interactions

arXiv:2605.22738v1 Announce Type: new Abstract: Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these hi

safetyarxiv-cs-lg

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces

arXiv:2605.22334v1 Announce Type: new Abstract: Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curve

Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

arXiv:2605.21490v1 Announce Type: new Abstract: We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequenc

Thermodynamic Irreversibility of Training Algorithms

arXiv:2605.21933v1 Announce Type: cross Abstract: The training algorithms for AI systems all introduce far-from-equilibrium dynamical processes, and understanding the irreversibility of these algorith

4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks

arXiv:2404.05307v2 Announce Type: replace Abstract: Reliable people detection is crucial for the safe autonomy of mobile robots and heavy vehicles, both on roads and in industrial settings like mining

AVI-HT: Adaptive Vision-IMU Fusion for 3D Hand Tracking

arXiv:2605.21714v1 Announce Type: new Abstract: We present AVI-HT, an adaptive visual-IMU fusion approach for tracking 3D hand poses by jointly modeling the egocentric image with on-glove 6-DoF IMU si

BodyReLux: Temporally Consistent Full-Body Video Relighting

arXiv:2605.21766v1 Announce Type: new Abstract: Being able to relight human performance is a fundamental task for post production and content creation. We present BodyReLux, a subject-specific video d

Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction

arXiv:2602.06676v4 Announce Type: replace Abstract: Fake Image Detection (FID), aiming at unified detection across four image forensic subdomains, is critical in real-world forensic scenarios. Compare

Codec-Robust Attacks on Audio LLMs

arXiv:2605.20519v1 Announce Type: cross Abstract: Prior attacks on Audio Large Language Models (Audio LLMs) demonstrated that carefully crafted waveform-domain perturbations can force targeted adversa

CryoNet: A Deep Learning Framework for Multi-Modal Debris-Covered Glacier Mapping. A Case Study of the Poiqu Basin, Central Himalaya

arXiv:2605.21527v1 Announce Type: cross Abstract: Glaciers play a critical role as freshwater reserves and indicators of climate change, yet their automatic delineation, especially for debris-covered

ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing

arXiv:2605.20802v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) exploit event-driven and addition-only computation to substantially improve efficiency for intelligent computation. A k

Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability

arXiv:2605.22273v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, but their adversarial robustness in visible-infrared (VI

GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations

arXiv:2605.22812v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, exi

HyperBench: Standardizing and Scaling Synthetic Evaluation for Hyperspectral Super-Resolution

arXiv:2605.21671v1 Announce Type: cross Abstract: Hyperspectral super-resolution (HSR) reconstructs a high-spatial-resolution hyperspectral image by fusing a low-resolution hyperspectral image (LR-HSI

Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance

arXiv:2605.22423v1 Announce Type: new Abstract: Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challe

MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy

arXiv:2605.22597v1 Announce Type: cross Abstract: Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physi

applicationsarxiv-cs-ro

Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission

arXiv:2605.20218v1 Announce Type: cross Abstract: Treating and preventing Human Immunodeficiency Virus (HIV) remains a critical global health challenge. While antiretroviral therapy provides a path to

Research

Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches

arXiv:2407.01734v4 Announce Type: replace-cross Abstract: Quantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in

researcharxiv-cs-ai