Search: “applications”

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding

arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elasti

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation

DGX agent

arXiv:2605.29357v1 Announce Type: new Abstract: Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long-tail

applicationsarxiv-cs-ai

29 May 2026

Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit

arXiv:2605.30207v1 Announce Type: new Abstract: The same prompt -- 'best CRM software' -- reaches AI assistants from buyers in widely different contexts: a solo founder, an enterprise VP, a UK SMB own

Safety

Quantum-Enhanced Adversarial Robustness in Artificial Intelligence

arXiv:2605.28899v1 Announce Type: cross Abstract: Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses si

safetyarxiv-cs-ai

Research

Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation

arXiv:2605.29911v1 Announce Type: cross Abstract: We propose a machine learning approach for image regression from sparse experimental measurements. We show the application of the proposed method on f

researcharxiv-cs-cv

Safety

REST3D: Reconstructing Physically Stable 3D Scenes from a Single Image

arXiv:2605.30338v1 Announce Type: new Abstract: Reconstructing physically stable 3D scenes from a single RGB image enables casual images to be converted into simulation-ready digital assets for applic

safetyarxiv-cs-cv

SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation

arXiv:2605.29662v1 Announce Type: new Abstract: Real-time inference of vision-language-action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for a

SAM3D-Phys: Towards Multi-Object Interactive Simulation in Real World

arXiv:2605.30239v1 Announce Type: new Abstract: This work addresses the problem of recovering complete, simulatable object geometry from reconstructed real-world scenes, enabling physics-based interac

Scalable RF Simulation in Generative 4D Worlds

arXiv:2508.12176v2 Announce Type: replace-cross Abstract: Radio Frequency (RF) sensing has emerged as a powerful, privacy-preserving alternative to vision-based methods for various perception tasks. H

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer

arXiv:2601.22274v2 Announce Type: replace Abstract: Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this set

Size Transferability of Graph Transformers with Convolutional Positional Encodings

arXiv:2602.15239v2 Announce Type: replace Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for g

Specialty-Specific Medical Language Model for Immune-Mediated Diseases

arXiv:2605.28838v1 Announce Type: cross Abstract: Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Termi

SurfFill: Completion of LiDAR Point Clouds via Gaussian Surfel Splatting

arXiv:2512.03010v2 Announce Type: replace Abstract: LiDAR-captured point clouds are often considered the gold standard in active 3D reconstruction. While their accuracy is exceptional in flat regions,

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

arXiv:2605.29526v1 Announce Type: cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addr

The New Pro Se: Generative AI and the Surge in Federal Civil Self-Representation

arXiv:2605.29493v1 Announce Type: cross Abstract: Since public access to generative AI tools became widespread, federal civil litigation has seen a marked increase in pro se (self-represented) plainti

VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models

arXiv:2605.29562v1 Announce Type: cross Abstract: Vision-Language-Action~(VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to uns

When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMs

arXiv:2605.29420v1 Announce Type: new Abstract: Persona prompting is widely used to steer large language models, yet its practical value remains unclear. Prior work often evaluates persona prompting u

Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues

arXiv:2605.30051v1 Announce Type: new Abstract: A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students,

Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia

arXiv:2506.08846v3 Announce Type: replace-cross Abstract: Automatic Speech Recognition (ASR) systems' growing use warrants robust auditing approaches to ensure equitable transcription quality, especia

AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness

arXiv:2605.28680v1 Announce Type: cross Abstract: The proliferation of Artificial Intelligence (AI) in workplaces is transforming how we work. While existing research on human-AI collaboration at work

Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs

arXiv:2601.12154v2 Announce Type: replace Abstract: This study investigates the use of neural topic modeling and LLMs to uncover meaningful themes from patient storytelling data, to offer insights tha

Architecture-driven Shift: towards a lightweight selector for capturing the trends of logit shift

arXiv:2605.27469v1 Announce Type: cross Abstract: Continual Learning (CL) is a practical paradigm to utilize power of deep pre-trained neural networks, but which pre-trained model has a better ability

AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems

arXiv:2605.27699v1 Announce Type: new Abstract: Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or n

applicationsarxiv-cs-ro

BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning

arXiv:2601.18116v2 Announce Type: replace Abstract: We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget

Tutorials

CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes

arXiv:2605.28125v1 Announce Type: new Abstract: Many real-world 3D reconstruction applications demand photorealism and metric accuracy across unbounded, complex scenes with challenging lighting and im

tutorialsarxiv-cs-cv

Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections

arXiv:2510.20480v2 Announce Type: replace Abstract: Accurate long-term localization using onboard sensors is crucial for robots operating in Global Navigation Satellite System (GNSS)-denied environmen

applicationsarxiv-cs-ro

Do readers prefer AI-generated Italian short stories?

arXiv:2601.17363v2 Announce Type: replace-cross Abstract: This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind

EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction

arXiv:2605.28101v1 Announce Type: cross Abstract: Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive s

FinBoardBench: Benchmarking Dynamic Wealth Management and Strategic Financial Reasoning of LLMs via Board Game Simulations

arXiv:2605.27896v1 Announce Type: new Abstract: Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However, exist

FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation

arXiv:2605.21832v2 Announce Type: replace Abstract: Modern recommender systems rely heavily on ID-based collaborative filtering: each item is represented by a unique ID embedding that accumulates coll

GEM: Generative Supervision Helps Embodied Intelligence

arXiv:2605.28548v1 Announce Type: new Abstract: Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Acti

GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease

arXiv:2605.27799v1 Announce Type: new Abstract: International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, prov

GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction

arXiv:2605.28645v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Grap

IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation

arXiv:2605.27397v1 Announce Type: new Abstract: In wireless sensor networks (WSNs), data augmentation is a novel method to improve sampling-frequency decision performance, thereby enabling energy opti

Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills?

arXiv:2605.27391v1 Announce Type: cross Abstract: This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship

Learning to target with network interference

arXiv:2605.27794v1 Announce Type: cross Abstract: This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others thr

LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments

arXiv:2605.27403v1 Announce Type: cross Abstract: Written reflection assignments give students valuable opportunities for critical self-assessment, meaning making, and learning processing. Additionall

Model Releases

MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution

model-releasesarxiv-cs-cl

arXiv:2605.28646v1 Announce Type: cross Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records,

Measuring Massive Multitask Chinese Understanding

arXiv:2304.12986v3 Announce Type: replace-cross Abstract: The development of large-scale Chinese language models is flourishing, yet there is a lack of corresponding capability assessments. Therefore,

Model Releases

MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content

model-releasesarxiv-cs-ai

arXiv:2605.28116v1 Announce Type: cross Abstract: Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from wh

Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors

arXiv:2605.27967v1 Announce Type: cross Abstract: Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), inclu

Model Releases

Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels

model-releasesarxiv-cs-ai

arXiv:2511.18894v5 Announce Type: replace-cross Abstract: Medical image segmentation is crucial for clinical applications, but it is frequently disrupted by noisy annotations and ambiguous anatomical

OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models

arXiv:2605.27916v1 Announce Type: cross Abstract: The advancement of general medical Multimodal Large Language Models (MLLMs) has shown great potential for building conversational assistants to suppor

Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning

arXiv:2605.28767v1 Announce Type: new Abstract: Many real-world classification tasks require predicting multiple labels per instance, necessitating the optimization of complex evaluation metrics such

QuITE: Query-Based Irregular Time Series Embedding

arXiv:2605.28166v1 Announce Type: cross Abstract: Irregular Multivariate Time Series (IMTS) are common in practice, yet their irregular sampling complicates effective modeling. Existing approaches typ

ResearchLoop: An Evidence-Gated Control Plane for AI-Assisted Research

arXiv:2605.28282v1 Announce Type: new Abstract: AI-assisted research compresses ideation, implementation, evaluation, and manuscript writing into a single interactive loop. This compression is useful,

SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

arXiv:2605.28084v1 Announce Type: cross Abstract: Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tas

Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier

arXiv:2212.04382v5 Announce Type: replace-cross Abstract: For a Bayes classifier whose input space is a graph, we study the structure of the boundary, which comprises those points for which at least o