Knowledge catalogue
arXiv:2601.08116v2 Announce Type: replace Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited datase
arXiv:2605.01089v1 Announce Type: new Abstract: The ensemble Gaussian mixture filter (EnGMF) is a powerful, convergent particle filter capable of medium-to-high dimensional non-linear filtering. The E
arXiv:2503.04496v2 Announce Type: replace-cross Abstract: In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within
arXiv:2605.02457v1 Announce Type: new Abstract: Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and h
arXiv:2601.17185v2 Announce Type: replace Abstract: We propose a new method for few-shot 3D reconstruction that integrates global and local frequency regularization to stabilize geometry and preserve
arXiv:2605.00879v1 Announce Type: new Abstract: Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other
arXiv:2605.02809v1 Announce Type: new Abstract: Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Re
arXiv:2202.10873v4 Announce Type: replace-cross Abstract: Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or b
arXiv:2605.01192v1 Announce Type: new Abstract: Two recent approaches to computation in superposition reach different recursive capacity regimes: Hanni et al. certify ilde{O}(d^{3/2}) computable featu
arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th
arXiv:2509.09152v2 Announce Type: replace Abstract: We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provide
arXiv:2605.01221v1 Announce Type: new Abstract: While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where nois
arXiv:2411.16821v5 Announce Type: replace Abstract: Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive de
arXiv:2605.02563v1 Announce Type: new Abstract: Road traffic accidents remain a significant global concern, with the majority attributed to human factors such as driver distraction and fatigue. This s
arXiv:2605.02167v1 Announce Type: cross Abstract: Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic prop
arXiv:2605.02629v1 Announce Type: new Abstract: Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on
arXiv:2605.02712v1 Announce Type: new Abstract: SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our
arXiv:2605.02260v1 Announce Type: cross Abstract: Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. W
arXiv:2601.12539v3 Announce Type: replace-cross Abstract: Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery
arXiv:2605.01386v1 Announce Type: new Abstract: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from informatio
arXiv:2605.01542v1 Announce Type: new Abstract: Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and Transformers, have become a new import
arXiv:2605.02052v1 Announce Type: new Abstract: This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples
Microsoft researchers share advances in building and operating large-scale distributed systems, spanning datacenters, networking, and the growing intersection with AI during NSDI ’26. The post Microso
arXiv:2605.02461v1 Announce Type: cross Abstract: Middle-mile logistics describes the problem of routing parcels through a network of hubs linked by trucks with finite capacity. We rephrase this as a
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:2605.00994v1 Announce Type: new Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors. To study these risks, rese
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