Search: “applications”

Riemannian Gradient Descent for Low-Rank Architectures

arXiv:2606.02328v1 Announce Type: new Abstract: We explore Riemannian optimization techniques for rank-factored matrix parameters, targeting contemporary deep learning applications. We examine ten poi

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RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection

DGX agent

arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime

model-releasesarxiv-cs-ai

2 Jun 2026

SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models

arXiv:2601.14323v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed in safety-critical robotic applications, yet their security vulnerabilities rema

safetyarxiv-cs-ai

SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence

arXiv:2606.02380v1 Announce Type: cross Abstract: As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications,

ToolFG: Towards Well-Grounded Fine-Grained Image Classification

arXiv:2606.02518v1 Announce Type: new Abstract: Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel para

safetyarxiv-cs-cv

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

model-releasesarxiv-cs-cv

arXiv:2509.16635v2 Announce Type: replace Abstract: In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, r

Trust Region On-Policy Distillation

arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent

safetyarxiv-cs-cl

Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention

arXiv:2602.07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the incre

X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding

model-releasesarxiv-cs-cv

arXiv:2606.02482v1 Announce Type: new Abstract: While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and mul

A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment

arXiv:2605.30989v1 Announce Type: new Abstract: Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has e

safetyarxiv-cs-ro

AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification

model-releasesarxiv-cs-lg

arXiv:2605.30786v1 Announce Type: new Abstract: Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have l

Local Ai

CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation

arXiv:2605.30668v1 Announce Type: cross Abstract: Dialogue topic segmentation is critical in many human-AI collaborative applications which requires identifying heterogeneous boundary cues, including

local-aiarxiv-cs-ai

Conformal Reliability: A New Evaluation Metric for Conditional Generation

arXiv:2605.30807v1 Announce Type: new Abstract: Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliabili

Cost-aware Stopping for Bayesian Optimization

arXiv:2507.12453v5 Announce Type: replace Abstract: In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive bla

safetyarxiv-cs-lg

Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch

arXiv:2511.17826v2 Announce Type: replace-cross Abstract: Deterministic inference is increasingly critical for large language model (LLM) applications such as LLM-as-a-judge evaluation, multi-agent sy

hardwarearxiv-cs-cl

DSD-GS: Dynamic-Static Decomposition of Gaussian Splatting for Efficient and High-Fidelity Dynamic Scene Reconstruction

arXiv:2605.30863v1 Announce Type: new Abstract: Dynamic scene reconstruction and novel view synthesis are fundamental to next-generation visual intelligence applications such as virtual reality, robot

hardwarearxiv-cs-cv

Functional Attention: From Pairwise Affinities to Functional Correspondences

arXiv:2605.31559v1 Announce Type: new Abstract: Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although tran

Tutorials

Generative Quantum Data Embeddings for Supervised Learning

arXiv:2605.30866v1 Announce Type: cross Abstract: Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are

tutorialsarxiv-cs-lg

GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent

arXiv:2603.13875v2 Announce Type: replace Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-ca

researcharxiv-cs-cl

Local Ai

Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation

arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and

local-aiarxiv-cs-ai

Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach

arXiv:2605.30903v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from mult

researcharxiv-cs-ai

Joint angle based learning to refine kinematic human pose estimation

arXiv:2507.11075v2 Announce Type: replace-cross Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in key

researcharxiv-cs-ai

Learning Agent-Compatible Context Management for Long-Horizon Tasks

arXiv:2605.30785v1 Announce Type: new Abstract: LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause lon

agentsarxiv-cs-ai

Local Ai

Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models

arXiv:2605.31158v1 Announce Type: new Abstract: Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time ga

local-aiarxiv-cs-cv

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone

agentsarxiv-cs-ai

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com

OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference

arXiv:2510.07651v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all

Tutorials

Procedural Generation of First Person Shooter Maps using Map-Elites

arXiv:2605.30570v1 Announce Type: new Abstract: We investigate the application of MAP-Elites (a well-known quality diversity algorithm) to design levels for First-Person Shooter (FPS) games. We consid

tutorialsarxiv-cs-ai

Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs

arXiv:2605.30646v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic var

SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer

arXiv:2605.30409v1 Announce Type: cross Abstract: Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formi

hardwarearxiv-cs-ai

Scalable Bayesian Inference for Nonlinear Conservation Laws

arXiv:2605.31127v1 Announce Type: new Abstract: Nonlinear conservation laws are at the heart of many of the most important dynamical systems in science and engineering. In practical applications, such

Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction

arXiv:2602.11802v2 Announce Type: replace Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fair

safetyarxiv-cs-lg

SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

arXiv:2605.30541v1 Announce Type: new Abstract: Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration

hardwarearxiv-cs-lg

TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion

model-releasesarxiv-cs-cl

arXiv:2601.09633v2 Announce Type: replace Abstract: Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce and semantic se

Triaging Threats to Specialized Guardrails

model-releasesarxiv-cs-cl

arXiv:2605.30693v1 Announce Type: cross Abstract: Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains

Unifying and Optimizing Data Values for Selection via Sequential Decision-Making

arXiv:2502.04554v2 Announce Type: replace Abstract: Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection

researcharxiv-cs-ai

What Does Preference Learning Recover from Pairwise Comparison Data?

arXiv:2602.10286v2 Announce Type: replace Abstract: Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical

When More Data Doesn't Help: Limits of Adaptation in Multitask Learning

arXiv:2601.20774v2 Announce Type: replace Abstract: Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set

A Complete Loss Landscape Analysis of Regularized Deep Matrix Factorization

arXiv:2506.20344v3 Announce Type: replace-cross Abstract: Despite its wide range of applications across various domains, the optimization foundations of deep matrix factorization (DMF) remain largely

A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning

model-releasesarxiv-cs-lg

arXiv:2605.29454v1 Announce Type: new Abstract: While Membership Inference Attacks (MIAs) are the prevailing method for identifying training data, their application has expanded into privacy auditing

A Systematic Evaluation of Molecular Mixture Behavior Prediction

arXiv:2605.29698v1 Announce Type: new Abstract: Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures wit

Apertus LLM Family Expansion via Distillation and Quantization

model-releasesarxiv-cs-lg

arXiv:2605.29128v1 Announce Type: new Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models

arXiv:2605.29754v1 Announce Type: new Abstract: Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Superv

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

arXiv:2605.30188v1 Announce Type: cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibr

Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent

arXiv:2603.01311v2 Announce Type: replace Abstract: The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for thi

agentsarxiv-cs-cl

Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing

arXiv:2605.29809v1 Announce Type: cross Abstract: Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intel

researcharxiv-cs-cv

Estimating the Empowerment of Language Model Agents

arXiv:2509.22504v3 Announce Type: replace Abstract: As language model (LM) agents become increasingly capable and adopted in real-world applications, there is a growing need for scalable evaluation fr

agentsarxiv-cs-ai

ExCAM: Explainable Cultural Awareness Metrics

model-releasesarxiv-cs-cl

arXiv:2605.29897v1 Announce Type: new Abstract: Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications