Search: “research”

Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models

arXiv:2506.19037v4 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) promise fast, non-autoregressive text generation, yet existing samplers, which pick tokens to unmask

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Research

Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks

DGX agent

arXiv:2602.02791v2 Announce Type: replace-cross Abstract: We study supervised multiclass classification for diffusion processes, where each class is characterized by a distinct drift function and traj

researcharxiv-cs-lg

14 May 2026

Polyhedral Instability Governs Regret in Online Learning

arXiv:2605.13692v1 Announce Type: new Abstract: Many online decision problems over combinatorial actions are addressed via convex relaxations, leading to online convex optimization with piecewise line

Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise

arXiv:2605.12648v1 Announce Type: new Abstract: We establish the first population risk bounds for Kolmogorov-Arnold Networks (KANs) trained by mini-batch SGD with gradient clipping, covering non-priva

Posterior Bayesian Neural Networks with Dependent Weights

arXiv:2507.22095v5 Announce Type: replace-cross Abstract: We consider fully connected and feedforward deep neural networks with dependent and possibly heavy-tailed weights, as introduced in [26], to a

Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI

arXiv:2508.14950v2 Announce Type: replace-cross Abstract: 4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its cl

Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference

arXiv:2602.06104v2 Announce Type: replace Abstract: Many engineering and scientific workflows rely on expensive black-box evaluations, requiring sequential decisions that must both improve task perfor

Preconditioned Flow Matching

arXiv:2603.02337v2 Announce Type: replace-cross Abstract: Flow matching (FM) learns vector fields by regressing stochastic velocity targets along intermediate distributions p_t. We identify a geometri

Predict-Project-Renoise: Sampling Diffusion Models under Hard Constraints

arXiv:2601.21033v2 Announce Type: replace Abstract: Diffusion models cannot enforce hard constraints, yet applications in the physical sciences demand exact satisfaction of conservation laws, boundary

Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy

arXiv:2605.12855v1 Announce Type: new Abstract: Clinical trial studies indicate benefit of watch-and-wait (WW) surveillance for patients with rectal cancer showing a complete or near clinical response

Prime Successor Irreducibility: Turing Machine Complexity, Kolmogorov Complexity, and Weakness-Based Formulations

arXiv:2605.12504v1 Announce Type: cross Abstract: We develop conjectures and theorems expressing the idea that the prime sequence exhibits computational irreducibility in the transition from one prime

PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

arXiv:2605.12851v1 Announce Type: cross Abstract: Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytopl

Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems

arXiv:2512.08411v2 Announce Type: replace Abstract: Model-based planning in robotic domains is challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete e

Profit Maximization in Bilateral Trade against a Smooth Adversary

arXiv:2605.12664v1 Announce Type: cross Abstract: Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem

Provable Quantization with Randomized Hadamard Transform

arXiv:2605.13810v1 Announce Type: new Abstract: Vector quantization via random projection followed by scalar quantization is a fundamental primitive in machine learning, with applications ranging from

Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising

arXiv:2605.12567v1 Announce Type: cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit no

QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling

arXiv:2605.13833v1 Announce Type: cross Abstract: Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through atten

QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification

arXiv:2512.16960v2 Announce Type: replace Abstract: Quantum-inspired machine learning (QiML) employs mathematical principles from quantum theory, such as Hilbert-space representations and quantum stat

Radial Compensation: Fixing Radius Distortion in Chart-Based Generative Models on Riemannian Manifolds

arXiv:2511.14056v2 Announce Type: replace-cross Abstract: We study the base distribution in chart-based generative models on Riemannian manifolds. Standard methods sample in Euclidean tangent space an

Re-evaluating Minimum Bayes Risk Decoding for Automatic Speech Recognition

arXiv:2510.19471v2 Announce Type: replace-cross Abstract: Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as ma

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

arXiv:2605.12813v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, motivating the need for realistic a

Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO

arXiv:2605.12578v1 Announce Type: cross Abstract: The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ab

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

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

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

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