Search: “research”

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation

arXiv:2510.14244v2 Announce Type: replace-cross Abstract: Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for addition

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Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing

DGX agent

arXiv:2605.13031v1 Announce Type: cross Abstract: This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target,

researcharxiv-cs-ro

14 May 2026

Repurposing Image Diffusion Models for Training-Free Music Style Transfer on Mel-spectrograms

arXiv:2411.15913v4 Announce Type: replace-cross Abstract: Music style transfer blends source structure with reference style to enable personalized music creation. However, existing zero-shot methods o

Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation

arXiv:2605.13129v1 Announce Type: cross Abstract: Recent 3D generative models can synthesize high-quality assets, but their outputs are typically static: they lack the skeletal rigs, joint hierarchies

RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization

arXiv:2603.20527v3 Announce Type: replace Abstract: Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of t

Robust and Explainable Bicuspid Aortic Valve Diagnosis Using Stacked Ensembles on Echocardiography

arXiv:2605.13730v1 Announce Type: cross Abstract: Transthoracic echocardiography (TTE) is the first-line imaging modality for diagnosing bicuspid aortic valve (BAV), yet diagnostic performance varies

Sample-Efficient Optimisation over the Outputs of Generative Models

arXiv:2509.23800v3 Announce Type: replace-cross Abstract: Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications,

Sampling from Flow Language Models via Marginal-Conditioned Bridges

arXiv:2605.13681v1 Announce Type: new Abstract: Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences.

Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions

arXiv:2310.07844v2 Announce Type: replace Abstract: We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against

researcharxiv-cs-ro

Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale

arXiv:2605.13684v1 Announce Type: new Abstract: We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensi

Scaling Laws for Mixture Pretraining Under Data Constraints

arXiv:2605.12715v1 Announce Type: new Abstract: As language models scale, the amount of data they require grows -- yet many target data sources, such as low-resource languages or specialized domains,

SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models

arXiv:2605.13667v1 Announce Type: new Abstract: Scene graph generation provides a compact structured representation for visual perception, but accurate and fast graph prediction from images and videos

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation

arXiv:2509.21543v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong promise for robotic task planning, particularly through the automatic generation of symbolic planning

researcharxiv-cs-ro

Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model

arXiv:2605.12945v1 Announce Type: new Abstract: Shortcut features are often invoked to explain out-of-distribution (OOD) failure, but training correlation, learned shortcut use, and test-time failure

Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators

arXiv:2605.12748v1 Announce Type: cross Abstract: Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI

sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging

arXiv:2510.11303v2 Announce Type: replace Abstract: Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semant

Spectral Flattening Is All Muon Needs: How Orthogonalization Controls Learning Rate and Convergence

arXiv:2605.13079v1 Announce Type: cross Abstract: Muon orthogonalizes the momentum buffer before each update, replacing its singular values with ones via Newton-Schulz iterations. This simple change l

Stable Attention Response for Reliable Precipitation Nowcasting

arXiv:2605.13181v1 Announce Type: cross Abstract: Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although

State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives

arXiv:2605.13127v1 Announce Type: cross Abstract: Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, co

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. W

Strategic PAC Learnability via Geometric Definability

arXiv:2605.13426v1 Announce Type: new Abstract: Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's dec

Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning

arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniforml

Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation

arXiv:2605.13333v1 Announce Type: cross Abstract: Text-driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine-level nuances of

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

arXiv:2602.23013v3 Announce Type: replace Abstract: Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achi

Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis

arXiv:2605.13312v1 Announce Type: new Abstract: We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes S

Support-Conditioned Flow Matching Is Kernel Smoothing

arXiv:2605.13386v1 Announce Type: new Abstract: Generative models are often conditioned on a small set of examples via cross-attention. Under the Gaussian optimal-transport path, we show that the exac

SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision

arXiv:2601.17326v2 Announce Type: replace Abstract: Retinal prostheses restore limited visual perception, but low spatial resolution and temporal persistence make reading difficult. In sequential lett

T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation

arXiv:2506.12075v2 Announce Type: replace-cross Abstract: High school English Literature teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning

Teaching and Learning under Deductive Errors

arXiv:2605.13384v1 Announce Type: new Abstract: Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language

The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities

arXiv:2510.04698v3 Announce Type: replace-cross Abstract: Humans systematically misrepresent probability in a stereotyped inverse-S pattern. It has been documented for decades, but its origin remains

The Diffusion Encoder

arXiv:2605.13399v1 Announce Type: new Abstract: We construct a new kind of encoder, leveraging the expressive power of diffusion models. In a traditional variational autoencoder, the encoder and decod

The Efficiency Gap in Byte Modeling

arXiv:2605.12928v1 Announce Type: new Abstract: Modern language models have historically relied on two dominant design choices: subword tokenization and autoregressive (AR) ordering. These design deci

The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models

arXiv:2605.12940v1 Announce Type: cross Abstract: Probabilistic Circuits (PCs) are deep generative models that support exact and efficient probabilistic inference. Yet in autoregressive language model

The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge

arXiv:2605.12908v1 Announce Type: cross Abstract: Weak-to-strong (W2S) generalization, in which a strong model is fine-tuned on outputs of a weaker, task-specialized model, has been proposed as an app

The Sample Complexity of Multiple Change Point Identification under Bandit Feedback

arXiv:2605.13252v1 Announce Type: cross Abstract: We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequent

The Score-Difference Flow for Implicit Generative Modeling

arXiv:2304.12906v4 Announce Type: replace Abstract: Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work

Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting

arXiv:2605.13678v1 Announce Type: new Abstract: Recent studies on long-term time series forecasting have shown that simple linear models and MLP-based predictors can achieve strong performance without

TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models

arXiv:2605.12518v1 Announce Type: cross Abstract: The proliferation of online news poses a challenge to extracting structured timelines from unstructured content. While recent studies have shown that

TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics

arXiv:2605.13748v1 Announce Type: new Abstract: Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existin

researcharxiv-cs-ro

TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation

arXiv:2605.13433v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-

U-HNO: A U-shaped Hybrid Neural Operator with Sparse-Point Adaptive Routing for Non-stationary PDE Dynamics

arXiv:2605.12965v1 Announce Type: new Abstract: Solutions to many partial differential equations (PDEs) display coexisting smooth global transport and localized sharp features within a single trajecto

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning

arXiv:2605.12700v1 Announce Type: new Abstract: Neural operators have become an effective framework for learning mappings between function spaces, yet most existing architectures realize operators wit

Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization

arXiv:2605.12756v1 Announce Type: cross Abstract: Large language models (LLMs) are pretrained by minimizing the cross-entropy loss for next-token prediction. In this paper, we study whether this optim

Understanding Generalization through Decision Pattern Shift

arXiv:2605.13148v1 Announce Type: cross Abstract: Understanding why deep neural networks (DNNs) fail to generalize to unseen samples remains a long-standing challenge. Existing studies mainly examine

Unified generalization analysis for physics informed neural networks

arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use

Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions

arXiv:2605.13158v1 Announce Type: new Abstract: Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather

Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation

arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic

Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction

arXiv:2605.13407v1 Announce Type: new Abstract: Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer int