Knowledge catalogue
arXiv:2605.13192v1 Announce Type: new Abstract: This paper presents a motion analysis framework for an athlete wearing sport-specific flexible prosthesis based on the soft-rigid hybrid-link system. Su
arXiv:2605.12753v1 Announce Type: cross Abstract: INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing re
arXiv:2605.11224v1 Announce Type: new Abstract: Existing medical-agent benchmarks deliver imaging as pre-selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiolo
arXiv:2605.11855v1 Announce Type: new Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term de
arXiv:2512.16875v4 Announce Type: replace-cross Abstract: We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution D an
arXiv:2604.02438v2 Announce Type: replace Abstract: The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios,
arXiv:2605.08193v1 Announce Type: cross Abstract: Normalization Equivariance (NE), equivariance to global contrast and brightness transforms, improves robustness to distribution shift in image-to-imag
arXiv:2605.08991v1 Announce Type: new Abstract: A series of papers has introduced the Heuristic Rating Estimation method, which evaluates a set of alternatives based on pairwise comparisons and the we
arXiv:2605.07950v1 Announce Type: new Abstract: We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target th
arXiv:2605.07116v1 Announce Type: cross Abstract: Physics-informed neural solvers offer a promising route to model-based reinforcement learning in continuous time, where optimal feedback synthesis is
arXiv:2605.05405v2 Announce Type: replace Abstract: Semantic search of Earth observation archives remains challenging. Visual foundation models such as CLAY produce rich embeddings of satellite imager
arXiv:2605.04126v1 Announce Type: new Abstract: This paper develops convolutional neural network (CNN) methods for simultaneous approximation and elliptic boundary value problems on compact Riemannian
arXiv:2601.00421v2 Announce Type: replace Abstract: This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in te
arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consiste
arXiv:2605.03690v1 Announce Type: new Abstract: We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss de
arXiv:2605.00276v1 Announce Type: new Abstract: Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting
arXiv:2512.09169v2 Announce Type: replace-cross Abstract: We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within
arXiv:2605.00870v1 Announce Type: cross Abstract: Wearable devices running Human Activity Recognition(HAR) on Inertial Measurement Units~(IMUs) waste energy by performing continuous classification for
arXiv:2601.00237v2 Announce Type: replace Abstract: This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-mod
arXiv:2605.02409v1 Announce Type: new Abstract: Bayesian Optimization is an iterative method, tailored to optimizing expensive black box objective functions. Surrogate models like Gaussian Processes,
arXiv:2605.02021v1 Announce Type: new Abstract: Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high-dimen
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:2605.00068v1 Announce Type: new Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively hi
arXiv:2604.27550v1 Announce Type: cross Abstract: Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and c
arXiv:2602.23163v3 Announce Type: replace Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanis
arXiv:2602.17166v2 Announce Type: replace Abstract: We present a robotics-oriented, coordinate-free formulation of inverse flight dynamics for fixed-wing aircraft on SO(3). Translational force balance
arXiv:2604.24775v1 Announce Type: cross Abstract: We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified fra
arXiv:2604.25119v1 Announce Type: new Abstract: Auditing the fine-tunes of open-weight generative models for harmful specialization has become a new governance challenge for model hosting platforms. T
arXiv:2110.12922v2 Announce Type: replace-cross Abstract: Laplace-type results characterize the limit of sequence of measures (pi_arepsilon)_{arepsilon >0} with density w.r.t the Lebesgue measure (d p
arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode
arXiv:2604.23083v1 Announce Type: cross Abstract: Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries betw
arXiv:2511.07410v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their depl
arXiv:2604.22580v1 Announce Type: cross Abstract: As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network pre
arXiv:2604.22679v1 Announce Type: cross Abstract: The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses includin
arXiv:2507.03806v3 Announce Type: replace-cross Abstract: This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and ex
arXiv:2604.21793v1 Announce Type: new Abstract: In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge
arXiv:2604.20735v1 Announce Type: new Abstract: Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertain
arXiv:2604.20030v1 Announce Type: new Abstract: Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. Howev
arXiv:2604.20472v1 Announce Type: cross Abstract: Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequenti
arXiv:2509.21020v2 Announce Type: replace Abstract: This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion pla
arXiv:2604.19633v1 Announce Type: new Abstract: Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. St
arXiv:2604.17763v1 Announce Type: cross Abstract: This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with impro
arXiv:2604.16490v1 Announce Type: new Abstract: Accurate brain image segmentation, particularly for distinguishing various tissues from magnetic resonance imaging (MRI) images, plays a pivotal role in
arXiv:2604.16851v1 Announce Type: cross Abstract: This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with
arXiv:2604.16783v1 Announce Type: new Abstract: Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end l
arXiv:2602.16213v2 Announce Type: replace-cross Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ic
arXiv:2509.11983v2 Announce Type: replace Abstract: Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlo
arXiv:2512.03053v2 Announce Type: replace-cross Abstract: We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain
arXiv:2511.13131v2 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they
In support of our mission to accelerate the developer journey on Google Cloud, we built Dev Signal: a multi-agent system designed to transform raw community signals into reliable technical guidance by
arXiv:2604.14809v1 Announce Type: cross Abstract: We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into
arXiv:2510.18935v3 Announce Type: replace Abstract: Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevan
arXiv:2604.13180v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliab
arXiv:2604.13763v1 Announce Type: cross Abstract: To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems
arXiv:2604.13240v1 Announce Type: new Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are
arXiv:2604.13795v1 Announce Type: new Abstract: Vision transformers (ViT) have been shown to allow for more flexible feature detection and can outperform convolutional neural network (CNN) when pre-tr
arXiv:2604.14118v1 Announce Type: new Abstract: Given two symmetric positive-definite matrices A, B in R^{n imes n}, we study the spectral properties of the interpolation A^{1-x} B^x for 0 leq x leq 1
arXiv:2604.13543v1 Announce Type: cross Abstract: Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are
arXiv:2604.12092v1 Announce Type: new Abstract: Behavior Trees (BTs) provide designers an intuitive graphical interface to construct long-horizon plans for autonomous systems. To ensure their correctn
arXiv:2601.16399v5 Announce Type: replace Abstract: We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optim