Search: “framework”

arXiv:2606.06996v1 Announce Type: new Abstract: This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safet

arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline

arXiv:2606.06250v1 Announce Type: new Abstract: Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-sui

arXiv:2110.06847v3 Announce Type: replace Abstract: From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogo

arXiv:2512.06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated b

arXiv:2606.04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability. Neither posture

arXiv:2606.00333v1 Announce Type: new Abstract: LLMs increasingly answer questions about taxes, labor protections, healthcare, education, pensions, and administrative procedures, where usefulness ofte

arXiv:2605.31231v1 Announce Type: cross Abstract: We present a neural-network-based framework for the solution of three-dimensional boundary value problems where the solution is expressible in terms o

arXiv:2605.31021v1 Announce Type: new Abstract: Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of

arXiv:2605.31167v1 Announce Type: new Abstract: Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite fo

arXiv:2502.16548v3 Announce Type: replace-cross Abstract: Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World

arXiv:2605.29943v1 Announce Type: cross Abstract: Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional

arXiv:2605.29939v1 Announce Type: cross Abstract: Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications,

arXiv:2605.29428v1 Announce Type: cross Abstract: We present DEtection in phase-folded Light curves with cOntrastive Scoring (DELOS), a contrastive-learning-based framework designed to search for shal

arXiv:2605.28619v1 Announce Type: new Abstract: In this work, we present a multiscale kinetic framework for consensus-based image segmentation. By interpreting an image as a system of interacting part

arXiv:2605.26476v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become critical for knowledge-intensive applications, yet evaluating its performance in vertical domains remain

arXiv:2605.23942v1 Announce Type: new Abstract: This paper proposes a structural and dynamical framework for modeling cognitive processes within a cybernetic perspective. Cognitive states are represen

arXiv:2605.24053v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. T

arXiv:2605.22604v1 Announce Type: cross Abstract: The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security

arXiv:2605.20276v1 Announce Type: new Abstract: The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud

arXiv:2601.20529v3 Announce Type: replace Abstract: Robotic prospecting for critical resources on the Moon, such as ilmenite, rare earth elements, and water ice, requires robust exploration methods gi

arXiv:2605.18898v1 Announce Type: new Abstract: We apply the Weibull distribution -- a two-parameter family from extreme-value theory -- as a diagnostic framework for element-wise weight magnitude dis

arXiv:2605.17608v1 Announce Type: cross Abstract: Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availabilit

arXiv:2605.17300v1 Announce Type: new Abstract: We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems. Coordinating multi-robot co

arXiv:2605.17390v1 Announce Type: cross Abstract: Context. Metamorphic Testing is recognised in IEEE/ISO software-testing standards and increasingly recommended for AI systems, but its progress is bot

arXiv:2605.17039v1 Announce Type: new Abstract: The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity

arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed

arXiv:2605.12706v1 Announce Type: new Abstract: RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the lim

arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from ele

arXiv:2605.13517v1 Announce Type: cross Abstract: Vector Quantized Variational Autoencoder (VQ-VAE) has become a fundamental framework for learning discrete representations in image modeling. However,

arXiv:2605.11247v1 Announce Type: new Abstract: This paper presents a proof-of-concept digital twin framework for simulation-driven diabetes modeling using benchmark clinical data, synthetic temporal

arXiv:2605.09483v1 Announce Type: cross Abstract: In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibili

arXiv:2605.10668v1 Announce Type: new Abstract: We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized a

arXiv:2605.10223v1 Announce Type: new Abstract: Current large language model agent frameworks prioritize autonomy but lack the governability mechanisms required for enterprise deployment. High-risk wr

arXiv:2604.15113v2 Announce Type: replace Abstract: Vector Symbolic Architectures (VSAs) provide a well-defined algebraic framework for compositional representations in hyperdimensional spaces. We int

arXiv:2605.08758v1 Announce Type: cross Abstract: Driven by the rapid expansion of e-commerce and small-batch production, the size of the intralogistics load unit of finished goods, semi-finished good

arXiv:2605.08935v1 Announce Type: new Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models

arXiv:2605.07188v1 Announce Type: new Abstract: We present PicoEyes, a unified gaze estimation framework that directly predicts all key attributes of gaze, including 3D eye parameters, eye-region segm

arXiv:2605.04076v1 Announce Type: new Abstract: U.S. financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks -- OCC Bulleti

arXiv:2605.05055v1 Announce Type: new Abstract: Localization in 5G and 6G networks is essential for important use cases such as intelligent transportation, smart factories, and smart cities. Although

arXiv:2405.16971v2 Announce Type: replace Abstract: Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by

arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse

arXiv:2605.00073v1 Announce Type: new Abstract: Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security audi

arXiv:2605.00937v1 Announce Type: cross Abstract: We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on

arXiv:2410.16636v2 Announce Type: replace-cross Abstract: We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accou

arXiv:2604.27673v1 Announce Type: new Abstract: We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents'), verbs (``Events'), and objects (``Targ