Search: “applications”

Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law

arXiv:2608.09393v1 Announce Type: cross Abstract: We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the ap

11 Aug 2026

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The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints

DGX agent

arXiv:2608.07980v1 Announce Type: cross Abstract: In recent years, the term voiceprint has regained attention, particularly in technological applications and policy-making contexts, often carrying the

safetyarxiv-cs-cl

11 Aug 2026

Tutorials

Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation

arXiv:2408.04619v2 Announce Type: replace-cross Abstract: The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, it

tutorialsarxiv-cs-ai

11 Aug 2026

Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation

arXiv:2608.07117v1 Announce Type: new Abstract: Radiology reporting is time-consuming and subject to inter-rater variability, making automated report generation an attractive clinical application for

Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

arXiv:2608.06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges

safetyarxiv-cs-lg

Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning

arXiv:2608.06938v1 Announce Type: cross Abstract: The visual reasoning ability of multimodal large language models (MLLMs) is crucial for downstream applications, particularly counter-commonsense reas

Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery

arXiv:2608.06406v1 Announce Type: new Abstract: Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosys

Online Conformal Prediction Beyond Feedback

arXiv:2608.07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provid

safetyarxiv-cs-lg

Agents

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

arXiv:2603.06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their r

agentsarxiv-cs-ai

Suppress and Diversify: Refining Robust Pathways for Corruption Robustness

arXiv:2608.06712v1 Announce Type: new Abstract: Model robustness against natural image corruptions is essential for safety-critical applications. While existing methods primarily focus on implicit rep

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

arXiv:2608.06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applic

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

model-releasesarxiv-cs-lg

arXiv:2608.05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential. Th

An Inertial Block Proximal Linearized Method with Adaptive Momentum for Nonconvex and Nonsmooth Optimization

arXiv:2608.05502v1 Announce Type: cross Abstract: In this paper, we consider a class of multiblock nonconvex nonsmooth optimization problems, which covers many applications such as the analysis of pre

Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset

arXiv:2608.06165v1 Announce Type: cross Abstract: Existing audio-to-score (A2S) systems primarily focus on classical music, and the application to popular music remains underexplored. This paper first

DREAM: LLM-based Dynamic Role-playing via Event-Aware Memory Graph

arXiv:2608.05170v1 Announce Type: cross Abstract: Role-playing agents (RPAs) have emerged as a key application of large language models, enabling immersive and high-fidelity character simulation. Accu

EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding

arXiv:2608.05303v1 Announce Type: cross Abstract: On-device deployment of Large Language Models (LLMs) has become essential for personalized edge applications. A primary bottleneck is external memory

local-aiarxiv-cs-cl

Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising

arXiv:2510.05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the v

LAWM-3D: Learning 3D-Aware Latent Actions from Human Videos for Generalizable Robot World Models

arXiv:2608.05706v1 Announce Type: new Abstract: World models enable agents to perform forward rollout and planning without real-world interaction. However, their application in open-world embodied int

safetyarxiv-cs-cv

Agents

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

arXiv:2608.05340v1 Announce Type: cross Abstract: Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-s

agentsarxiv-cs-ai

One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs

arXiv:2512.14751v3 Announce Type: replace-cross Abstract: Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications. However, its secur

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

arXiv:2607.27934v2 Announce Type: replace Abstract: SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known

SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2608.06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance,

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

arXiv:2608.06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application

Worst-Case Distance-Aware Error Bounds for Neural Networks

arXiv:2510.22021v3 Announce Type: replace Abstract: Safety-critical applications of machine learning require uncertainty estimates that support reliable worst-case analysis. Neural networks (NNs) prov

safetyarxiv-cs-lg

A Chain Is Only as Strong as Its Weakest Link: A Scoping Review of System Integration Audits in AI

arXiv:2608.04921v1 Announce Type: cross Abstract: As AI systems become increasingly integrated into diverse interfaces and applications, model-centric audits are insufficient to address risks arising

safetyarxiv-cs-ai

Agents

AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering

arXiv:2411.04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep lea

agentsarxiv-cs-ai

Beyond Motion Cues and Structural Sparsity: Revisiting Small Moving Target Detection

arXiv:2509.07654v2 Announce Type: replace Abstract: Small moving target detection is crucial for many defense applications but remains highly challenging due to low signal-to-noise ratios, ambiguous v

local-aiarxiv-cs-cv

CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers

arXiv:2608.04035v1 Announce Type: cross Abstract: The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Base

safetyarxiv-cs-ai

Exo2EgoPose: Leveraging Exocentric Demonstrations for Vision-Language guided Egocentric 3D Hand Pose Forecasting

arXiv:2607.15890v2 Announce Type: replace Abstract: Perceiving multimodal cues and forecasting fine-grained actions from an egocentric (Ego) perspective is vital for applications like robot manipulati

researcharxiv-cs-cv

FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

arXiv:2511.07322v3 Announce Type: replace-cross Abstract: While LLMs have shown great success in financial tasks like stock prediction and question answering, their application in fully automating Equ

Link prediction on multi-relational graphs from an influence propagation perspective

arXiv:2608.05016v1 Announce Type: cross Abstract: Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction predictio

local-aiarxiv-cs-lg

LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching

arXiv:2608.04106v1 Announce Type: new Abstract: Dense image matching establishes pixel-wise correspondences and underpins broad applications in computer vision and photogrammetry. However, extending d

Tutorials

Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

arXiv:2607.18559v2 Announce Type: replace-cross Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajecto

tutorialsarxiv-cs-lg

OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing

arXiv:2608.05049v1 Announce Type: new Abstract: Instruction-based video editing (IVE) is an emerging field with broad applications, yet evaluating editing models remains challenging. Existing benchmar

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality

arXiv:2608.03487v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a wid

Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation

model-releasesarxiv-cs-cl

arXiv:2601.22124v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because pr

Training-Free Hashing-Based Attention via Binary Principal Components

arXiv:2608.04405v1 Announce Type: cross Abstract: Long-context large language models (LLMs) are increasingly deployed in real-world applications, yet self-attention remains a major efficiency bottlene

local-aiarxiv-cs-ai

TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model

arXiv:2608.04174v1 Announce Type: new Abstract: Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks fo

Why Ranking Anomaly Detection Algorithms Isn't as Reliable as You May Think

model-releasesarxiv-cs-lg

arXiv:2608.04613v1 Announce Type: new Abstract: Anomaly detection is a safety-critical machine learning problem with applications ranging from fraud detection to network intrusion prevention and indus

A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces

arXiv:2608.03103v1 Announce Type: cross Abstract: Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation. However, their application to co

safetyarxiv-cs-ai

Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation

arXiv:2608.03166v1 Announce Type: new Abstract: Role-Playing Language Agents (RPLAs) are increasingly deployed in high-stakes applications such as healthcare assistance, customer support, and educatio

ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction

model-releasesarxiv-cs-cl

arXiv:2608.03154v1 Announce Type: new Abstract: Biomedical relation extraction (BioRE) extracts structured knowledge from biomedical literature for applications such as knowledge base construction and

Bi-Lipschitz Ansatz for Anti-Symmetric Functions

arXiv:2503.04263v2 Announce Type: replace Abstract: Motivated by applications to the simulation of quantum many-body systems by neural networks, researchers have suggested several models which are ant

Bridging Online and Offline Handwriting via Differentiable Physical Rendering

arXiv:2608.03198v1 Announce Type: new Abstract: Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic calli

Can LLMs Test Terminal User Interfaces?

arXiv:2608.03743v1 Announce Type: cross Abstract: Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools

Deep Divide-and-Reduce in Symbolic Regression

arXiv:2608.02628v1 Announce Type: cross Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions. Current machin

EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners

arXiv:2608.03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only re

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

arXiv:2608.03826v1 Announce Type: new Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations,