Search: “models”

HQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation

arXiv:2604.06715v1 Announce Type: cross Abstract: Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scen

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Research

Hybrid CNN-Transformer Architecture for Arabic Speech Emotion Recognition

DGX agent

arXiv:2604.07357v1 Announce Type: new Abstract: Recognizing emotions from speech using machine learning has become an active research area due to its importance in building human-centered applications

researcharxiv-cs-cl

10 Apr 2026

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation

arXiv:2405.03420v2 Announce Type: cross Abstract: This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using gradient-based Ne

Interpretable Tau-PET Synthesis from Multimodal T1-Weighted and FLAIR MRI Using Partial Information Decomposition Guided Disentangled Quantized Half-UNet

arXiv:2602.22545v2 Announce Type: replace Abstract: Tau positron emission tomography (tau-PET) is an important in vivo biomarker of Alzheimer's disease, but its cost, limited availability, and acquisi

Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents

arXiv:2510.07809v4 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) empower autonomous mobile agents, yet their security under realistic mobile deployment constraints remain

Iterative Formalization and Planning in Partially Observable Environments

arXiv:2505.13126v3 Announce Type: replace-cross Abstract: Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve

Applications

Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands

arXiv:2509.18455v4 Announce Type: replace Abstract: Nonprehensile manipulation, such as pushing and pulling, enables robots to move, align, or reposition objects that may be difficult to grasp due to

applicationsarxiv-cs-ro

Learning to Query History: Nonstationary Classification via Learned Retrieval

arXiv:2604.07027v1 Announce Type: new Abstract: Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout

local-aiarxiv-cs-lg

LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller

arXiv:2512.19576v5 Announce Type: replace-cross Abstract: Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model

agentsarxiv-cs-ai

Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding

arXiv:2508.20765v2 Announce Type: replace-cross Abstract: The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increa

Low-Rank Key Value Attention

arXiv:2601.11471v3 Announce Type: replace Abstract: The key-value (KV) cache is a primary memory bottleneck in Transformers. We propose Low-Rank Key-Value (LRKV) attention, which reduces KV cache memo

researcharxiv-cs-lg

Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning

arXiv:2604.07345v1 Announce Type: cross Abstract: The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the e

hardwarearxiv-cs-lg

MemReader: From Passive to Active Extraction for Long-Term Agent Memory

arXiv:2604.07877v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction

safetyarxiv-cs-cl

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems

arXiv:2604.06881v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However,

researcharxiv-cs-lg

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining

arXiv:2604.06956v1 Announce Type: cross Abstract: Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from

hardwarearxiv-cs-lg

Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

arXiv:2604.01204v2 Announce Type: replace-cross Abstract: Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstru

ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents

arXiv:2604.07789v1 Announce Type: cross Abstract: Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agenti

agentsarxiv-cs-cl

OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance

arXiv:2604.08461v1 Announce Type: new Abstract: Open-Vocabulary Segmentation (OVS) aims to segment image regions beyond predefined category sets by leveraging semantic descriptions. While CLIP based a

safetyarxiv-cs-cv

Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech

arXiv:2512.24517v2 Announce Type: replace Abstract: Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentat

Applications

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations

arXiv:2604.06620v1 Announce Type: new Abstract: Quantitative estimation of wheel polygonal roughness from axle-box vibration signals is a challenging yet practically relevant problem for rail-vehicle

applicationsarxiv-cs-lg

Tutorials

Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting

arXiv:2503.09640v2 Announce Type: replace-cross Abstract: Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applicat

tutorialsarxiv-cs-cv

RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing

arXiv:2512.06774v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a leading representation for high-fidelity 3D assets, yet protecting these assets via digital watermarking r

hardwarearxiv-cs-cv

ReCellTy: Domain-Specific Knowledge Graph Retrieval-Augmented LLMs Reasoning Workflow for Single-Cell Annotation

arXiv:2505.00017v2 Announce Type: replace Abstract: With the rapid development of large language models (LLMs), their application to cell type annotation has drawn increasing attention. However, gener

RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs

arXiv:2510.19225v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows i

hardwarearxiv-cs-lg

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob

safetyarxiv-cs-ro

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs

arXiv:2604.05650v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Specu

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning

arXiv:2604.06636v1 Announce Type: cross Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from

Shortcut Learning in Glomerular AI: Adversarial Penalties Hurt, Entropy Helps

arXiv:2604.07936v1 Announce Type: new Abstract: Stain variability is a pervasive source of distribution shift and potential shortcut learning in renal pathology AI. We ask whether lupus nephritis glom

safetyarxiv-cs-cv

SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

arXiv:2604.08377v1 Announce Type: cross Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after depl

agentsarxiv-cs-cl

SleepNet and DreamNet: Enriching and Reconstructing Representations for Consolidated Visual Classification

arXiv:2409.01633v4 Announce Type: replace-cross Abstract: An effective integration of rich feature representations with robust classification mechanisms remains a key challenge in visual understanding

Applications

SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation

arXiv:2506.11786v2 Announce Type: replace Abstract: Accurate real-time estimation of human movement dynamics, including internal joint moments and muscle forces, is essential for applications in clini

applicationsarxiv-cs-lg

SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation

arXiv:2604.07101v1 Announce Type: cross Abstract: We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response

SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems

arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches ofte

SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation

arXiv:2604.08405v1 Announce Type: new Abstract: Diffusion-based audio-driven talking-head generation enables realistic portrait animation, but also introduces risks of misuse, such as fraud and misinf

Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection

arXiv:2604.06332v1 Announce Type: cross Abstract: Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking dist

agentsarxiv-cs-lg

Time-Series Classification with Multivariate Statistical Dependence Features

arXiv:2604.06537v1 Announce Type: new Abstract: In this paper, we propose a novel framework for non-stationary time-series analysis that replaces conventional correlation-based statistics with direct

researcharxiv-cs-lg

Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

arXiv:2604.06251v1 Announce Type: new Abstract: This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the

Towards provable probabilistic safety for scalable embodied AI systems

arXiv:2506.05171v3 Announce Type: replace-cross Abstract: Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of sy

UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding

arXiv:2507.22025v4 Announce Type: replace Abstract: The emergence of Multimodal Large Language Models (MLLMs) has driven significant advances in Graphical User Interface (GUI) agent capabilities. Neve

agentsarxiv-cs-ai

URMF: Uncertainty-aware Robust Multimodal Fusion for Multimodal Sarcasm Detection

arXiv:2604.06728v1 Announce Type: cross Abstract: Multimodal sarcasm detection (MSD) aims to identify sarcastic intent from semantic incongruity between text and image. Although recent methods have im

WebSP-Eval: Evaluating Web Agents on Website Security and Privacy Tasks

arXiv:2604.06367v1 Announce Type: cross Abstract: Web agents automate browser tasks, ranging from simple form completion to complex workflows like ordering groceries. While current benchmarks evaluate

When Fine-Tuning Changes the Evidence: Architecture-Dependent Semantic Drift in Chest X-Ray Explanations

arXiv:2604.08513v1 Announce Type: new Abstract: Transfer learning followed by fine-tuning is widely adopted in medical image classification due to consistent gains in diagnostic performance. However,

WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects

arXiv:2604.07759v1 Announce Type: new Abstract: Ship detection for navigation is a fundamental perception task in intelligent waterway transportation systems. However, existing public ship detection d

Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting

arXiv:2608.12590v1 Announce Type: new Abstract: Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these

Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents

arXiv:2608.12764v1 Announce Type: cross Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per t

Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation?

arXiv:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapt

Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

arXiv:2608.13391v1 Announce Type: new Abstract: Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation b

safetyarxiv-cs-cv