Knowledge catalogue
arXiv:2610.06805v1 Announce Type: new Abstract: Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models
arXiv:2610.05139v1 Announce Type: new Abstract: Code large language model (Code LLM) assistants generate code from heterogeneous development contexts, including open files, imported modules, pasted sn
arXiv:2603.26954v2 Announce Type: replace Abstract: The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and th
arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener
arXiv:2610.05135v1 Announce Type: new Abstract: Under a fixed physical law, the visible geometry of a scene determines how motion must change. We ask how video generators realize this relationship. We
arXiv:2610.04988v1 Announce Type: new Abstract: Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance betwee
arXiv:2610.06034v1 Announce Type: cross Abstract: We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB)
arXiv:2610.05757v1 Announce Type: new Abstract: This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM)
arXiv:2610.05463v1 Announce Type: cross Abstract: Visual search is a fundamental cognitive ability. This study investigates whether Multimodal Large Language Models (MLLMs) exhibit human-like difficul
arXiv:2610.06146v1 Announce Type: cross Abstract: Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particula
arXiv:2610.06675v1 Announce Type: new Abstract: We study gradient descent (GD) with a large constant stepsize for logistic regression on linearly separable data. Existing analysis shows an accelerated
arXiv:2511.18199v2 Announce Type: replace-cross Abstract: Ecological Momentary Assessment (EMA) studies provide real-time data on suicidal thoughts and behaviors, but forecasting suicide attempts rema
arXiv:2610.06505v1 Announce Type: cross Abstract: Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent. They should provide approp
arXiv:2610.05997v1 Announce Type: new Abstract: Early development unfolds in caregiver-infant dyads, where infants' sensorimotor streams are shaped by physical contact and face-to-face interaction. Ye
arXiv:2610.04273v1 Announce Type: new Abstract: We study supervised classification problems with missing feature values. Existing approaches often decouple imputation from classification, producing im
arXiv:2610.06128v1 Announce Type: new Abstract: Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models t
arXiv:2610.06630v1 Announce Type: cross Abstract: CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators direc
arXiv:2610.06018v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time. Existing STVG mo
arXiv:2610.04727v1 Announce Type: new Abstract: In worker-equipment proximity monitoring, video transformers are widely used for collision anticipation and have demonstrated strong performance. Howeve
arXiv:2610.04396v1 Announce Type: cross Abstract: Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers. How
arXiv:2610.05967v1 Announce Type: new Abstract: Image compression, restoration, and generation all require a way to measure how different two images look to a person. Pixel error ignores how people se
arXiv:2610.06234v1 Announce Type: new Abstract: Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classifi
arXiv:2610.05549v1 Announce Type: new Abstract: We formulate joint estimation of common-slope decay rates and amplitudes from room impulse responses (RIRs) as parameterized nonnegative matrix factoriz
arXiv:2610.04786v1 Announce Type: cross Abstract: Time-series foundation models buy zero-shot forecasting with large temporal corpora; a vision model needs none, since a natural image implicitly embed
arXiv:2610.06588v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation relies on persistent memory to maintain scene consistency. Existing systems follow two strategies to ach
arXiv:2610.06349v1 Announce Type: new Abstract: Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often ado
arXiv:2610.05250v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper
arXiv:2610.03842v1 Announce Type: new Abstract: Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose unifo
arXiv:2610.05987v1 Announce Type: new Abstract: Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, t
arXiv:2610.04183v1 Announce Type: cross Abstract: Language models exhibit remarkable robustness, continuing to produce coherent text even when their activations are perturbed by interventions like lin
arXiv:2610.05915v1 Announce Type: new Abstract: This paper revisits the distributed learning problem for training a multinomial logistic regression model with the Federated Averaging (exttt{FedAvg}) a
arXiv:2610.04385v1 Announce Type: cross Abstract: Many decision trees fit the same data almost equally well, yet they can route a query point to different leaves and induce different local empirical d
arXiv:2610.06070v1 Announce Type: cross Abstract: Normalized gradient descent is a widely studied adaptive optimization method. Most existing analyses focus on the best iterate or a weighted average o
arXiv:2610.06468v1 Announce Type: new Abstract: Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become c
arXiv:2610.06355v1 Announce Type: cross Abstract: Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Lat
arXiv:2610.04034v1 Announce Type: new Abstract: Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods fa
arXiv:2610.05349v1 Announce Type: new Abstract: We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas sta
arXiv:2610.04703v1 Announce Type: cross Abstract: Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation. However,
arXiv:2505.23615v2 Announce Type: replace Abstract: Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be
arXiv:2508.17512v2 Announce Type: replace Abstract: Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational effi
arXiv:2610.03978v1 Announce Type: new Abstract: Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino a
arXiv:2610.06006v1 Announce Type: new Abstract: We study contextual queueing bandits, where a learner schedules jobs while learning unknown service rates modeled by logistic functions of job-server fe
arXiv:2610.03812v1 Announce Type: cross Abstract: A time-series forecast is scored on a future value of the series. A representation loss that regresses the next latent, as in LeNEPA, is a different l
arXiv:2610.04518v1 Announce Type: new Abstract: Length generalization is the ability of sequential models to perform well on context lengths unseen during training. In this work, we show that the chal
arXiv:2610.05538v1 Announce Type: new Abstract: We introduce Loop Flow Transformers (LiFT), a family of looped generative models that scales computation by repeatedly applying a shared Diffusion Trans
arXiv:2610.06580v1 Announce Type: new Abstract: Spike-and-slab regression is a standard Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are
arXiv:2610.04291v1 Announce Type: cross Abstract: Sparse causal discovery calls for methods that exploit graph structure without estimating high-dimensional densities. We introduce Fisher Information
arXiv:2610.05174v1 Announce Type: cross Abstract: A kernel k(x,y) is LSHable if there exists a locality sensitive hashing scheme H such that k(x,y)=Pr_{hsim H}[h(x)=h(y)] for all x,y. This notion play
arXiv:2610.04708v1 Announce Type: new Abstract: Operator learning methods such as DeepONets and FNOs often struggle with PDE families featuring sharp interfaces, heterogeneous coefficients, and locali
arXiv:2610.03741v1 Announce Type: cross Abstract: Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks becaus
arXiv:2610.04785v1 Announce Type: new Abstract: Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However,
arXiv:2610.03771v1 Announce Type: new Abstract: We propose a fine-tuning method for flow-matching diffusion models aimed at realistic artificial light modeling without the need for a large training da
arXiv:2610.06095v1 Announce Type: new Abstract: Operator-learning benchmarks are stored at full precision and have grown to terabyte scale. Rate-distortion theory says how many bits the stored field n
arXiv:2610.04695v1 Announce Type: new Abstract: Operator eigenvalue problems appear throughout science. Classical methods usually discretize the operator into a matrix and then solve the resulting mat
arXiv:2610.04326v1 Announce Type: new Abstract: Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints witho
arXiv:2610.06133v1 Announce Type: new Abstract: Journal entry anomaly detectors are commonly evaluated on the full population with ROC-AUC, precision and recall, ignoring the review budget and which a
arXiv:2606.17345v2 Announce Type: replace Abstract: Although pitch sequencing is a central topic in baseball analytics, previous studies have primarily focused on improving predictive performance for
arXiv:2610.04419v1 Announce Type: new Abstract: Generative models are often trained with isotropic objectives such as mean-squared error. For data concentrated near a low-dimensional manifold, however
arXiv:2610.06046v1 Announce Type: new Abstract: Magnetic navigation provides contact-free and line-of-sight-independent feedback for robotic systems, yet existing approaches typically rely on explicit
arXiv:2610.04510v1 Announce Type: cross Abstract: Diffusion models for structured scientific generation must produce samples satisfying hard geometric constraints imposed by physics, chemistry, or bio