Knowledge catalogue
arXiv:2605.01520v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning
arXiv:2605.01987v1 Announce Type: new Abstract: We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of extit{subsampling stability}. We derive upper bounds
arXiv:2605.01676v1 Announce Type: cross Abstract: Missing data imputation remains a fundamental challenge in modern data science, especially when uncertainty quantification is essential. In this work,
arXiv:2605.01766v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have revolutionized the landscape of AI, demonstrating impressive capabilities in tackling complex vision and
arXiv:2605.02438v1 Announce Type: new Abstract: Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based me
arXiv:2605.02521v1 Announce Type: new Abstract: Affective image editing (AIE) aims to edit visual content to evoke target emotions. However, existing methods often overlook inference efficiency and pr
arXiv:2605.01725v1 Announce Type: new Abstract: Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computat
arXiv:2605.02689v1 Announce Type: new Abstract: Long-term time series forecasting requires models that simultaneously capture rapid oscillations, medium-range periodicities, and slowly evolving macro-
arXiv:2605.00885v1 Announce Type: new Abstract: Existing single image dehazing methods have demonstrated satisfactory performance on homogeneous thin-haze images; however, they often struggle with non
arXiv:2605.01563v1 Announce Type: new Abstract: We propose a unified cross-domain transfer learning framework that leverages knowledge from multiple heterogeneous medical imaging datasets to improve p
arXiv:2508.09193v2 Announce Type: replace Abstract: Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlli
arXiv:2605.02437v1 Announce Type: new Abstract: Objective: Accurate probability estimates are essential for the safe deployment of medical image segmentation models in clinical decision-making. Howeve
arXiv:2602.03668v2 Announce Type: replace-cross Abstract: Latent actions learned from diverse human videos serve as pseudo-labels for vision-language-action (VLA) pretraining, but provide effective su
arXiv:2502.00204v3 Announce Type: replace Abstract: We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the
arXiv:2605.02044v1 Announce Type: new Abstract: Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports r
arXiv:2605.01263v1 Announce Type: cross Abstract: We study query time bounds for the fundamental problem of estimating the kernel mean frac1{|X|}sum_{xin X}mathbf{k}(x,y) of a query y in a finite data
arXiv:2605.01745v1 Announce Type: cross Abstract: Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or acc
arXiv:2510.23633v2 Announce Type: replace-cross Abstract: Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation informatio
arXiv:2604.03380v2 Announce Type: replace Abstract: Generating diverse, pedagogically valid stories for Arabic early-grade reading assessments requires balancing tight constraints on vocabulary, readi
arXiv:2605.02212v1 Announce Type: new Abstract: This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutio
arXiv:2605.02054v1 Announce Type: cross Abstract: This paper presents a dual quaternion framework for 6-DOF visual target tracking that addresses key limitations of perspective-n-point (PnP) solvers:
arXiv:2603.29258v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that
arXiv:2605.01506v1 Announce Type: new Abstract: Recent advances in omni-modal large language models have enabled remarkable progress in joint vision-audio understanding. However, prevailing architectu
arXiv:2605.00889v1 Announce Type: new Abstract: We investigate the explanability properties of the recently proposed linear-min-max neural networks. At initialization, they can be interpreted as k-med
arXiv:2605.02572v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While p
arXiv:2510.02898v5 Announce Type: replace Abstract: Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on pai
arXiv:2605.01749v1 Announce Type: new Abstract: Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors
arXiv:2410.03801v5 Announce Type: replace Abstract: A new Kolmogorov-Arnold network (KAN) is proposed to approximate potentially irregular functions in high dimensions. We provide error bounds for thi
arXiv:2511.06838v4 Announce Type: replace-cross Abstract: The substantial memory bandwidth and computational demands of large language models (LLMs) present critical challenges for efficient inference
arXiv:2507.21114v3 Announce Type: replace-cross Abstract: Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges
arXiv:2605.02692v1 Announce Type: cross Abstract: The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recu
arXiv:2605.01632v1 Announce Type: new Abstract: Models that are indistinguishable on in-distribution data can behave very differently under distribution shift. We introduce Perturb-and-Correct (P&C),
arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp
arXiv:2512.07578v3 Announce Type: replace-cross Abstract: Global SHAP explanations are typically presented as feature-importance rankings, which identify variables that matter to a black-box model but
arXiv:2605.00973v1 Announce Type: new Abstract: Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, m
arXiv:2605.01003v1 Announce Type: cross Abstract: Statistical change point (CP) detection methods typically rely on likelihood-based inference and ignore contextual information about plausible CP loca
arXiv:2605.02863v1 Announce Type: new Abstract: Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS), which are resource-intensive to collect and fail to provide interp
arXiv:2605.00834v1 Announce Type: new Abstract: The algebraic diversity framework replaces temporal averaging over multiple observations with algebraic group action on a single observation for second-
arXiv:2512.05525v2 Announce Type: replace-cross Abstract: Businesses increasingly rely on large language models (LLMs) to automate simple repetitive tasks instead of developing custom machine learning
arXiv:2605.02500v1 Announce Type: new Abstract: Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and
arXiv:2605.02050v1 Announce Type: cross Abstract: This work establishes a foundational framework for standardizing AI evaluation RCTs (sometimes called human uplift studies). Drawing on established ex
arXiv:2512.00748v2 Announce Type: replace Abstract: Lesion segmentation is inherently influenced by imaging uncertainty, arising from ill-defined lesion boundaries and inter-observer variability in di
arXiv:2512.06938v2 Announce Type: replace Abstract: Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this p
arXiv:2511.07372v3 Announce Type: replace Abstract: Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving
arXiv:2405.10271v4 Announce Type: replace Abstract: The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large mode
arXiv:2605.02720v1 Announce Type: cross Abstract: Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets tha
arXiv:2511.07328v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and
arXiv:2605.01168v1 Announce Type: new Abstract: It is increasingly recognized that human annotators do not always agree, and such disagreement is inherent in many annotation tasks. However, not all in
arXiv:2404.11277v2 Announce Type: replace-cross Abstract: In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for ind
arXiv:2605.01395v1 Announce Type: cross Abstract: In this paper, we design feedback control laws for soft robots modelled using the Cosserat rod, which is spatially discretised using the Piecewise Con
arXiv:2605.02693v1 Announce Type: cross Abstract: Across many scientific disciplines, multiple observations are collected from the same experimental units, and in modern datasets these observations of
arXiv:2605.01393v1 Announce Type: new Abstract: Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries th
arXiv:2605.01072v1 Announce Type: cross Abstract: We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based
arXiv:2605.00971v1 Announce Type: cross Abstract: Background: Sensitivity of AI-assisted lung nodule detection systems is known to vary with CT acquisition parameters including radiation dose, reconst
arXiv:2603.05140v2 Announce Type: replace-cross Abstract: We characterise the computational power of recurrent graph neural networks (GNNs) in terms of arithmetic circuits over the real numbers. Our n
arXiv:2605.01827v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently demonstrated their proficiency in holistic visual comprehension. However, most of them struggle to tackle r
arXiv:2511.17340v3 Announce Type: replace Abstract: Generative image models can produce convincingly real images, with plausible shapes, textures, layouts and lighting. However, one domain in which th
arXiv:2602.04514v3 Announce Type: replace Abstract: The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional represen
arXiv:2605.00915v1 Announce Type: new Abstract: Standard representation probing for visual models relies on mathematically permutation-invariant operations like Global Average Pooling (GAP) or CLS tok
arXiv:2605.02627v1 Announce Type: new Abstract: Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromat