research

DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation

arXiv:2411.14295v3 Announce Type: replace Abstract: Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video

Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching

DGX agent

arXiv:2605.00941v1 Announce Type: cross Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem. Existing

researcharxiv-cs-cv

5 May 2026

DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity

arXiv:2605.01852v1 Announce Type: new Abstract: Multi-view 3D reconstruction, namely, structure-from-motion followed by multi-view stereo, is a fundamental component of 3D computer vision. In general,

Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks

arXiv:2605.02416v1 Announce Type: cross Abstract: In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for LEO satellite networks. The pro

DynamicPO: Dynamic Preference Optimization for Recommendation

arXiv:2605.00327v1 Announce Type: cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferen

researcharxiv-cs-ai

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

arXiv:2605.01238v1 Announce Type: cross Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning e

Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback

arXiv:2506.18315v2 Announce Type: replace-cross Abstract: LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies h

researcharxiv-cs-ai

Embody4D: A Generalist 4D World Model for Embodied AI

arXiv:2605.01799v1 Announce Type: new Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representat

Enforcing tail calibration when training probabilistic forecast models

arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated

Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM

arXiv:2605.01315v1 Announce Type: new Abstract: This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Usi

Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning

arXiv:2605.02378v1 Announce Type: new Abstract: In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins

arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-s

Evolutionary BP+OSD Decoding for Low-Latency Quantum Error Correction

arXiv:2512.18273v2 Announce Type: replace-cross Abstract: Quantum error correction (QEC) for fault-tolerant quantum computing requires a balanced decoding solution that offers high performance, low co

researcharxiv-cs-ai

Exact Loop Controllers for ReLU Realization of Homogeneous Curve Refinements

arXiv:2605.01655v1 Announce Type: cross Abstract: We study homogeneous refinement operators ((Vgamma)(t)=sum_{jinmathbb Z}A_jgamma(Mt-j)), acting on compactly supported continuous piecewise linear cur

Extending machine learning model for implicit solvation to free energy calculations

arXiv:2510.20103v2 Announce Type: replace-cross Abstract: The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its a

Extrapolation in Statistical Learning with Extreme Value Theory

arXiv:2605.01909v1 Announce Type: cross Abstract: Extreme value theory provides rigorous theory and statistical tools for extrapolation in machine learning, particularly in settings where traditional

FEAT: Fashion Editing and Try-On from Any Design

arXiv:2605.02393v1 Announce Type: new Abstract: Fashion design aims to express a designer's creative intent and to depict how garments interact with the human body. Recent methods condition on multimo

Feature Weighting Improves Pool-Based Sequential Active Learning for Regression

arXiv:2604.02019v2 Announce Type: replace Abstract: Pool-based sequential active learning for regression (ALR) optimally selects a small number of samples sequentially from a large pool of unlabeled s

Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training

arXiv:2605.02374v1 Announce Type: cross Abstract: Machine-generated text (MGT) detection is critical for regulating online information ecosystems, yet existing detectors often underperform in few-shot

Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection

arXiv:2605.01596v1 Announce Type: new Abstract: This paper describes the system submitted by team extbf{Archaeology} to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of

Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients

arXiv:2605.01702v1 Announce Type: new Abstract: Theoretical studies show that for any differentiable function on a compact domain, there exists a neural network that approximates both the function val

Focus and Dilution: The Multi-stage Learning Process of Attention

arXiv:2605.01199v1 Announce Type: new Abstract: Transformer-based models have achieved remarkable success across a wide range of domains, yet our understanding of their training dynamics remains limit

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

arXiv:2605.01367v1 Announce Type: cross Abstract: High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlate

From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination

arXiv:2605.01656v1 Announce Type: cross Abstract: Human cognition emerges from coordinated spiking dynamics in distributed neural circuits, where information is encoded via both firing rates and preci

From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity

arXiv:2605.00939v1 Announce Type: new Abstract: Traditional hallucination detection fails on 'Stubborn Hallucinations' -- errors where LLMs are confidently wrong. We propose a geometric solution: Embe

From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals

arXiv:2605.01616v1 Announce Type: new Abstract: Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartpho

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments

arXiv:2605.02098v1 Announce Type: new Abstract: Large-scale 3D point clouds can consist of billions of points. Even after downsampling, these point clouds are too large for modern 3D neural networks.

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

arXiv:2605.02789v1 Announce Type: cross Abstract: Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initializati

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

arXiv:2502.06719v3 Announce Type: replace-cross Abstract: In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the S

GEASS: Training-Free Caption Steering for Hallucination Mitigation in Vision-Language Models

arXiv:2605.01733v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at grounded reasoning but remain prone to object hallucination. Recent work treats self-generated captions as a unif

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

GEODE: Angle-Adaptive OOD Detection with Universal Scorer Compatibility

arXiv:2605.01063v1 Announce Type: cross Abstract: Outlier Exposure (OE) is among the strongest training-based OOD detectors on standard benchmarks but exhibits scorer-dependent tradeoffs (e.g., strong

Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection

arXiv:2605.02616v1 Announce Type: new Abstract: Salient Object Detection (SOD) remains an essential yet underexplored task in the era of large-scale vision models. Although foundation models like SAM

Gradient Boosted Risk Scores

arXiv:2605.02593v1 Announce Type: new Abstract: Risk scores are an interpretable and actionable class of machine learning models with applications in medicine, insurance, and risk management. Unlike m

Gradient-Discrepancy Acquisition for Pool-Based Active Learning

arXiv:2605.02609v1 Announce Type: new Abstract: The effectiveness of active learning hinges on the choice of the acquisition criterion by which a learning algorithm selects potentially informative dat

Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation

arXiv:2605.02376v1 Announce Type: new Abstract: Automated medical report generation, MRG, holds substantial value for alleviating radiologist workload and enhancing diagnostic efficiency. However, mai

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning

arXiv:2605.01310v1 Announce Type: new Abstract: Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence su

GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

arXiv:2605.01688v1 Announce Type: new Abstract: Long-horizon conversational agents rely on memory systems with increasingly sophisticated retrieval mechanisms. However, retrieved fragments are typical

GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting

arXiv:2405.15491v4 Announce Type: replace Abstract: We present GSDeformer, a method that enables cage-based deformation on 3D Gaussian Splatting (3DGS). Our approach bridges cage-based deformation and

Hallucination Detection in LLMs with Topological Divergence on Attention Graphs

arXiv:2504.10063v4 Announce Type: replace Abstract: Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models (LLMs). We introduce TOHA, a TOp

Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping

arXiv:2601.17467v2 Announce Type: replace Abstract: Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination det

Heavy-Tailed Principal Component Analysis

arXiv:2603.11308v2 Announce Type: replace Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order mom

HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation

arXiv:2605.02278v1 Announce Type: new Abstract: Time series imputation benefits from leveraging cross-feature correlations, yet existing attention-based methods re-discover feature relationships at ea

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models

arXiv:2506.06057v2 Announce Type: replace Abstract: Large Language Models (LLMs) rely on massive training datasets, often including proprietary data, which raises concerns about unauthorized usage and

HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction

arXiv:2508.09179v3 Announce Type: replace-cross Abstract: Reconstructing high-fidelity MR images from undersampled k-space data remains a challenging problem in MRI. While Mamba variants for vision ta