Knowledge catalogue
arXiv:2609.34897v1 Announce Type: new Abstract: Whole slide image classification is a fundamental task in computational pathology, where patch representation quality directly affects downstream aggreg
arXiv:2609.34326v1 Announce Type: new Abstract: Large Language Model (LLM) routers commonly rely on neural query embeddings, with larger encoders expected to better capture query intent and difficulty
arXiv:2609.34527v1 Announce Type: new Abstract: We introduce the first real-time reflection-aware Gaussian SLAM system for indoor scenes. The system features a reflection-aware TSDF-Gaussian hybrid re
arXiv:2609.33011v1 Announce Type: new Abstract: We study contextual dueling bandits with general function approximation under the Bradley-Terry-Luce (BTL) preference model. A key challenge in this set
arXiv:2609.33641v1 Announce Type: cross Abstract: Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas tur
arXiv:2609.34499v1 Announce Type: new Abstract: Graph neural networks (GNNs) excel at representation learning on Knowledge Graphs (KGs), achieving stateof-the-art performance on tasks like link predic
arXiv:2609.32183v1 Announce Type: new Abstract: High-resolution plant imaging enables detailed characterization of plant morphology, but dense scientific analysis remains limited by costly pixel-level
arXiv:2609.32837v1 Announce Type: new Abstract: Ultrasound (US) acquisition depends on the operator's ability to interpret anatomy and anticipate how the view will change with probe motion. Many robot
arXiv:2609.33518v1 Announce Type: new Abstract: Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens com
arXiv:2609.32640v1 Announce Type: cross Abstract: We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning inte
arXiv:2510.21330v2 Announce Type: replace Abstract: Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling meth
arXiv:2609.33179v1 Announce Type: cross Abstract: Constrained-option scoring reads a model's probabilities for a fixed set of permitted answers, so it returns a score even when the model is about to w
arXiv:2609.33694v1 Announce Type: new Abstract: Vision-language models (VLMs) answer visual questions by combining visual information extraction with downstream problem solving. We investigate a funda
arXiv:2609.32456v1 Announce Type: new Abstract: World modeling under partial observation requires reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded obje
arXiv:2609.33780v1 Announce Type: new Abstract: Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route dive
arXiv:2602.07919v2 Announce Type: replace-cross Abstract: Text guided diffusion models are used by millions of users, but can be easily exploited to produce harmful content. Concept unlearning methods
arXiv:2609.32268v1 Announce Type: new Abstract: Standard autoencoder (AE) inference uses a single encoder-decoder pass, though the latent may not be optimal for each sample under a fixed decoder. We a
arXiv:2609.31788v1 Announce Type: new Abstract: Progress in 3D CT report generation is usually sought in increasingly sophisticated architectures and larger pools of training data. We find instead tha
arXiv:2602.05464v2 Announce Type: replace-cross Abstract: Conditional representation learning aims to extract criterion-specific features for customized tasks. Recent methods construct conditional sub
arXiv:2609.34479v1 Announce Type: new Abstract: Vision-language (VL) pretraining using paired chest X-ray (CXR) images and radiology reports has shown strong potential for medical image understanding.
arXiv:2609.34801v1 Announce Type: new Abstract: Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. Thi
arXiv:2609.32529v1 Announce Type: new Abstract: Open-prompt detectors allow users to specify targets with text, visual exemplars, or both. We argue that existing designs underuse the visual modality i
arXiv:2609.34538v1 Announce Type: new Abstract: Looped Transformers reuse a shared block across recurrent depths, making autoregressive decoding expensive because every generated token requires many s
arXiv:2609.33154v1 Announce Type: cross Abstract: We study episodic continuous-time control with an unknown vector control gain, scalar state, quadratic action cost, and smooth convex terminal cost. I
arXiv:2508.08552v3 Announce Type: replace Abstract: System heterogeneity in federated learning (FL) requires training workloads and communication demands to be adapted to client resource capacities. F
arXiv:2609.32279v1 Announce Type: new Abstract: Efficiently learning and sampling from high dimensional, multimodal unnormalized distributions without target samples remains a challenging problem. Alt
arXiv:2609.33947v1 Announce Type: cross Abstract: Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which dimi
arXiv:2609.35553v1 Announce Type: new Abstract: Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state. This iterative refinement
arXiv:2606.19504v2 Announce Type: replace Abstract: Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational
arXiv:2609.33112v1 Announce Type: new Abstract: Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertain
arXiv:2609.34907v1 Announce Type: cross Abstract: Bathroom acoustic-event recognition can support ambient assisted living in settings where continuous video monitoring is undesirable. However, practic
arXiv:2609.32847v1 Announce Type: cross Abstract: We propose sliced Orlicz-Wasserstein (SOW) distance which is a generalization of sliced Wasserstein (SW) distance. SOW replaces the L^p norm in SW wit
arXiv:2609.35161v1 Announce Type: new Abstract: We present an in-depth investigation of how a form of Bayesian reasoning about common causes can emerge as a cross-contextual generalization in small, t
arXiv:2603.00717v3 Announce Type: replace Abstract: Recent advances in generative models have exposed a fundamental weakness of image fake detectors: they often fail to generalize to unseen synthesis
arXiv:2609.34071v1 Announce Type: new Abstract: Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network e
arXiv:2609.33311v1 Announce Type: cross Abstract: Humanoid robots are increasingly expected to serve as embodied social agents that communicate naturally with humans through face-to-face interaction.
arXiv:2609.33510v1 Announce Type: new Abstract: Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generat
arXiv:2609.33550v1 Announce Type: cross Abstract: Automatic relevance determination (ARD) is the standard device for input selection in Gaussian process (GP) regression. By giving the covariance kerne
arXiv:2609.34124v1 Announce Type: new Abstract: Cross-view spatial reasoning requires a model to align different viewpoints into a coherent spatial representation, yet this ability remains challenging
arXiv:2609.34111v1 Announce Type: cross Abstract: The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive
arXiv:2609.35410v1 Announce Type: new Abstract: Spectral super-resolution of multispectral satellite images can enable high temporal- and spatial-resolution hyperspectral satellite imagery at a modest
arXiv:2609.34977v1 Announce Type: new Abstract: Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computation
arXiv:2609.35097v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN fore
arXiv:2609.34153v1 Announce Type: new Abstract: Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge
arXiv:2506.07932v2 Announce Type: replace-cross Abstract: We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained encoders and decoders to compre
arXiv:2609.33408v1 Announce Type: new Abstract: In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90% missing data. An attention-based base
arXiv:2609.33761v1 Announce Type: new Abstract: Generative models for multivariate weather series are routinely evaluated with pooled distributional metrics computed after marginal calibration. We sho
arXiv:2609.35758v1 Announce Type: cross Abstract: We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects
arXiv:2609.31712v1 Announce Type: new Abstract: Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) wi
arXiv:2609.34056v1 Announce Type: new Abstract: Reasoning models often act as if they pursue goals, but their efforts are not always directed toward what users intend, sometimes leading them to pursue
arXiv:2609.35002v1 Announce Type: new Abstract: Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whethe
arXiv:2603.26852v2 Announce Type: replace-cross Abstract: We propose novel algorithms for sequence prediction based on ideas from stringology. These algorithms are time and space efficient and satisfy
arXiv:2609.34025v1 Announce Type: cross Abstract: We present a new class of near-linear algorithms for efficiently integrating general tensor fields defined on trees with distance dependent kernels, t
arXiv:2609.35502v1 Announce Type: new Abstract: Multimodal prediction relies on diverse forms of evidence: information repeated across modalities, cues specific to a single source, and complex cross-m
arXiv:2609.34831v1 Announce Type: new Abstract: Neural Stochastic Differential Equations (Neural SDEs) provide flexible continuous-time generative models, but generic neural drift and diffusion networ
arXiv:2609.32766v1 Announce Type: new Abstract: Modern machine learning spans supervised, transfer, continual, meta-learning, and related regimes that often reuse the same hypothesis classes, architec
arXiv:2609.35005v1 Announce Type: cross Abstract: Keyword Spotting (KWS) is becoming increasingly important as voice-controlled devices grow more widespread. While voice interaction with smartphones a
arXiv:2609.35177v1 Announce Type: new Abstract: Estimating integrals of black-box, high-dimensional functions, from expectations and kernel mean embeddings to the softmax kernel in self-attention, is
arXiv:2609.34502v1 Announce Type: new Abstract: We present SubjectAnchor, a Subject-Aware Memory-to-Video paradigm for multi-shot storytelling in which the current shot is generated by conditioning on
arXiv:2609.34884v1 Announce Type: new Abstract: Post-training quantization reduces the deployment cost of vision-language models (VLMs), but preserving multimodal capabilities at low bit widths remain