Knowledge catalogue
arXiv:2609.08461v1 Announce Type: cross Abstract: Parametric PDEs can admit different boundary conditions with different accuracy and computational cost. We introduce a framework for learning when one
arXiv:2609.06530v1 Announce Type: new Abstract: Continual learning is a crucial capability for Graphical User Interface (GUI) agents to adapt to evolving applications while retaining knowledge acquire
arXiv:2609.05859v1 Announce Type: cross Abstract: Learning with class-conditional label noise often relies on a transition model from latent clean classes to observed annotations. Forward correction e
arXiv:2609.07415v1 Announce Type: new Abstract: We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently
arXiv:2609.07982v1 Announce Type: new Abstract: Standard semi-supervised learning (SSL) typically relies on labelled and unlabelled data sharing a common marginal distribution. This assumption is ofte
arXiv:2609.06419v1 Announce Type: new Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based m
arXiv:2609.05742v1 Announce Type: new Abstract: American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion representat
arXiv:2601.17090v3 Announce Type: replace Abstract: Partial differential equations (PDEs) govern complex systems, yet neural operators often struggle to efficiently capture the long-range, nonlocal in
arXiv:2609.07868v1 Announce Type: cross Abstract: We study the training dynamics of multiclass logistic regression on high-dimensional Gaussian mixture models with a large number of classes and establ
arXiv:2609.07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisance
arXiv:2609.06547v1 Announce Type: new Abstract: Real-time 3D mapping is fundamental for autonomous robotic navigation, with Euclidean Signed Distance Fields (ESDFs) serving as the standard representat
arXiv:2609.06296v1 Announce Type: new Abstract: Self-supervised sign language representation learning must model two properties not central to natural-image SSL: signs are produced by a small set of a
arXiv:2609.08496v1 Announce Type: new Abstract: Sign language video generation demands precise hand and facial articulation, yet modern video diffusion models, trained predominantly on spoken-language
arXiv:2609.09152v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined nonnegative stepsizes in smooth convex optimization. Writing p_{sil}=log_2(
arXiv:2609.08200v1 Announce Type: new Abstract: Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing a
arXiv:2609.05533v1 Announce Type: new Abstract: Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier
arXiv:2609.07085v1 Announce Type: new Abstract: Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion
arXiv:2609.07269v1 Announce Type: new Abstract: This paper develops a singularity-free guiding vector field (SF-GVF) for path following on the special orthogonal group SO(3). First, we lift the Euclid
arXiv:2609.05521v1 Announce Type: cross Abstract: The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such case
arXiv:2401.09895v3 Announce Type: replace Abstract: One of the fundamental challenges in cell microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions
arXiv:2609.06718v1 Announce Type: new Abstract: Humanoid soccer is a challenging testbed for dynamic whole-body control, requiring robots to coordinate balance, locomotion, object interaction, and ski
arXiv:2609.05861v1 Announce Type: cross Abstract: One important AI-for-RAN use case is energy saving, in which radio resources and cell energy modes must be dynamically controlled without violating us
arXiv:2609.07445v1 Announce Type: new Abstract: Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperat
arXiv:2609.06906v1 Announce Type: cross Abstract: We develop a new low-accuracy sampler, called smoothed Picard Hamiltonian Monte Carlo, which combines Gaussian smoothing, Picard iteration, and higher
arXiv:2609.07394v1 Announce Type: new Abstract: Anticipating whether a person will interact from one's own perspective is a highly intuitive task for humans, that relies on a combination of cues. We i
arXiv:2609.08221v1 Announce Type: new Abstract: Micro-actions are subtle, low-intensity non-verbal behaviors that provide cues to fine-grained human states, including emotions and intentions. Recogniz
arXiv:2609.06187v1 Announce Type: new Abstract: As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art a
arXiv:2609.07590v1 Announce Type: new Abstract: We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be tra
arXiv:2609.07608v1 Announce Type: new Abstract: Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density,
arXiv:2609.07983v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a mes
arXiv:2609.08133v1 Announce Type: new Abstract: In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirica
arXiv:2604.05788v2 Announce Type: replace Abstract: Radio maps are important for environment-aware wireless communication, network planning, and radio resource optimization. However, dense radio map c
arXiv:2609.06109v1 Announce Type: new Abstract: Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations
arXiv:2609.06426v1 Announce Type: new Abstract: Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjunctio
arXiv:2609.06232v1 Announce Type: new Abstract: Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision sig
arXiv:2609.07718v1 Announce Type: cross Abstract: Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is r
arXiv:2609.07064v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D
arXiv:2603.05581v2 Announce Type: replace Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility dema
arXiv:2609.06186v1 Announce Type: new Abstract: Prioritized Sweeping (PS) accelerates model-based reinforcement learning by selecting backups according to Bellman residual magnitude. In nonstationary
arXiv:2609.08219v1 Announce Type: cross Abstract: Neural networks acquire internal representations through learning. In this work, we formulate stochastic gradient descent (SGD) as a Markovian stochas
arXiv:2609.07571v1 Announce Type: new Abstract: 360{eg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP)
arXiv:2609.09012v1 Announce Type: new Abstract: Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas th
arXiv:2609.07933v1 Announce Type: new Abstract: High-quality demonstration data is becoming a central bottleneck for training general-purpose humanoid robots. While recent humanoid teleoperation syste
arXiv:2510.08999v2 Announce Type: replace Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight prunin
arXiv:2507.18073v2 Announce Type: replace Abstract: Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can s
arXiv:2601.10324v3 Announce Type: replace Abstract: Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the w
arXiv:2609.05661v1 Announce Type: new Abstract: We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam,
arXiv:2609.06430v1 Announce Type: new Abstract: We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Statis
arXiv:2609.07148v1 Announce Type: cross Abstract: While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability
arXiv:2609.05916v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substan
arXiv:2609.07165v1 Announce Type: new Abstract: Learning-by-Demonstration (LbD) enables intuitive robot programming by capturing expert skills, which is crucial for agility in high-mix, low- volume ma
arXiv:2609.07512v1 Announce Type: new Abstract: Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challen
arXiv:2609.06289v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference
arXiv:2609.06951v1 Announce Type: new Abstract: Activation steering promises modular control of language model behavior: a behavior such as politeness corresponds to a direction in a model's activatio
arXiv:2609.06473v1 Announce Type: new Abstract: Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization
arXiv:2609.06580v1 Announce Type: cross Abstract: We investigate stochastic simple bilevel optimization with smooth and possibly nonconvex upper- and lower-level objectives. Existing stochastic extens
arXiv:2609.08412v1 Announce Type: new Abstract: Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at fa
arXiv:2609.05956v1 Announce Type: new Abstract: Spatial transcriptomics (ST) provides unprecedented insights into tumor heterogeneity by capturing spatially resolved gene expression, yet its high expe
arXiv:2601.14862v2 Announce Type: replace Abstract: We introduce Strategic Doctrine Language Models (sdLM), a learning-system framework for multi-document strategic reasoning with doctrinal consistenc
arXiv:2609.07956v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-thro