Knowledge catalogue
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
arXiv:2510.25239v2 Announce Type: replace Abstract: Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microc
arXiv:2610.06472v1 Announce Type: new Abstract: We address the challenge of accurate 3D object reconstruction from multi-view images in Gaussian Splatting. Existing object-level 3DGS methods reconstru
arXiv:2610.03991v1 Announce Type: new Abstract: Multimodal skin lesion classification combines clinical images with patient metadata to improve diagnostic accuracy. However, complete metadata availabl
arXiv:2603.23533v3 Announce Type: replace-cross Abstract: Markdown carries structure a parser reads for free: headers, section paths, and block boundaries. Many RAG pipelines also spend LLM calls per
arXiv:2610.04422v1 Announce Type: new Abstract: Guided diffusion samplers are increasingly used to reconstruct physical fields from sparse observations, but standard diagnostics do not say how much of
arXiv:2610.05196v1 Announce Type: new Abstract: Risk regulation imposes directional constraints on scores; we adopt their strict per-input form -- the score monotone non-decreasing in every exposure i
arXiv:2610.05954v1 Announce Type: cross Abstract: Reward post-training of flow models either reweights the model's own samples under a KL penalty or a frozen reference, often for thousands of updates,
arXiv:2610.06582v1 Announce Type: cross Abstract: World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronousl
arXiv:2610.05277v1 Announce Type: cross Abstract: Code language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during
arXiv:2610.04157v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) often struggle to capture long-range dependencies due to over-squashing -- a phenomenon in which the repeated compression
arXiv:2610.04310v1 Announce Type: new Abstract: Machine unlearning is seen as a promising approach to enable users to exercise the 'right to erasure' in the context of AI models. We ask how users migh
arXiv:2610.05552v1 Announce Type: new Abstract: Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which few cl
arXiv:2610.03898v1 Announce Type: new Abstract: Model-based control of an aquatic robotic platform depends on a hydrodynamic model that is costly to identify and specific to the hull, payload, and con
arXiv:2610.04814v1 Announce Type: new Abstract: Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from one fixed image segmentation. This hides t
arXiv:2512.12694v2 Announce Type: replace-cross Abstract: Large multilingual knowledge bases expose temporally diverse information, yet retrieval quality remains sensitive to lexical variation and cro
arXiv:2610.06389v1 Announce Type: new Abstract: Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks i
arXiv:2610.06602v1 Announce Type: new Abstract: Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-c
arXiv:2610.04722v1 Announce Type: new Abstract: Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, maki
arXiv:2610.04944v1 Announce Type: cross Abstract: We characterize, up to logarithmic factors, the minimax expected query complexity of finite-sum nonconvex-strongly-concave optimization under mean-squ
arXiv:2610.06502v1 Announce Type: new Abstract: Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, an
arXiv:2610.04551v1 Announce Type: new Abstract: Multiple LiDAR-inertial odometry methods have been widely applied in robotic applications owing to their enhanced accuracy and reliability. However, ado
arXiv:2610.05919v1 Announce Type: new Abstract: Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving e
arXiv:2610.05081v1 Announce Type: new Abstract: Teleoperation is becoming increasingly important for collecting high-quality demonstrations to teach robots dexterous manipulation skills. For dexterous
arXiv:2610.04924v1 Announce Type: new Abstract: Visually identical containers can conceal loads that require different handling decisions. We present TANav, which uses a brief nudge to measure push re
arXiv:2610.06139v1 Announce Type: cross Abstract: Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning model
arXiv:2610.05186v1 Announce Type: cross Abstract: We prove that no single rank ordered adversary strategy is globally optimal for the prediction with expert advice problem with six or more experts, in