Knowledge catalogue
arXiv:2607.22258v1 Announce Type: cross Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks. However, their performanc
arXiv:2607.21848v1 Announce Type: new Abstract: Recent conditional video generation models have shown promising potentials to transform 3D engine renderings, such as depth maps and untextured geometry
arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal p
arXiv:2509.10058v2 Announce Type: replace Abstract: Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design,
arXiv:2607.22078v1 Announce Type: new Abstract: As autonomous driving systems move toward real-world deployment, interpretable, behavior-level decision-making is essential for safety, trust, and regul
arXiv:2607.22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are other
arXiv:2607.21654v1 Announce Type: cross Abstract: The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes. It offers a minimally r
arXiv:2607.22409v1 Announce Type: new Abstract: Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from s
arXiv:2607.22062v1 Announce Type: cross Abstract: This paper addresses the modeling and synthesis of constrained robotic system behaviors using Petri nets (PNs). It investigates how to construct model
arXiv:2607.22221v1 Announce Type: cross Abstract: Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be
arXiv:2510.05416v3 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks.
arXiv:2607.22386v1 Announce Type: new Abstract: Latent domain watermarking for diffusion models embeds watermarks directly into the latent prior, enjoying non-intrusiveness to model parameters and sea
arXiv:2510.26369v2 Announce Type: replace-cross Abstract: Logistics warehouses have struggled with labor shortages, but the inbound processes remain particularly human-powered. Worker location data is
arXiv:2607.22012v1 Announce Type: new Abstract: Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and
arXiv:2601.12311v2 Announce Type: replace-cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space
arXiv:2607.22334v1 Announce Type: cross Abstract: Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through
arXiv:2607.21823v1 Announce Type: new Abstract: We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the la
arXiv:2607.21861v1 Announce Type: new Abstract: We study baking documents directly into the weights of a 4-bit Gemma-4-e4b model via LoRA, so a system can answer questions about a corpus closed-book:
arXiv:2607.22123v1 Announce Type: new Abstract: Visual inertial odometry (VIO) is essential for accurate 6-DoF motion estimation in mobile robotic systems. Recent learning-based VIO methods have shown
arXiv:2607.22165v1 Announce Type: cross Abstract: LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison. We identify four gaps between evaluation a
arXiv:2607.22035v1 Announce Type: new Abstract: Currently, most foundation models can reproduce or strongly depend on copyrighted training content, but output similarity alone is insufficient for infr
arXiv:2601.17303v2 Announce Type: replace Abstract: As Industrial Internet of Things (IIoT) environments scale to tens of thousands of connected devices, centralized security architectures introduce l
arXiv:2607.22212v1 Announce Type: new Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and
arXiv:2607.21745v1 Announce Type: cross Abstract: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep
arXiv:2601.10554v3 Announce Type: replace Abstract: The efficacy of autonomous driving systems hinges critically on robust prediction and planning capabilities. However, current benchmarks are impeded
arXiv:2607.22446v1 Announce Type: new Abstract: Recent radiance field methods represent scenes with 2D primitives that offer surface alignment and efficient rasterization, from Gaussian disks to trian
arXiv:2607.22249v1 Announce Type: new Abstract: Nociception is a protective biological mechanism that links harmful stimulation to a reaction. This paper investigates artificial nociception for a robo
arXiv:2607.22041v1 Announce Type: new Abstract: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particular
arXiv:2607.21904v1 Announce Type: cross Abstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. I
arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather
arXiv:2512.03424v4 Announce Type: replace Abstract: State Space Models (SSMs) model long token sequences of point cloud with linear complexity, but require an unordered point cloud to be serialized. E
arXiv:2607.21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning
arXiv:2607.21627v1 Announce Type: cross Abstract: End-to-end reinforcement learning can improve the accuracy of compound LLM systems, but it does not constrain how modules divide labor internally. We
arXiv:2607.21617v1 Announce Type: cross Abstract: Vision Language Models (VLMs) are increasingly used in place of traditional OCR pipelines for document understanding. In this paper, we show they do n
arXiv:2607.22148v1 Announce Type: new Abstract: In this work, we aim to discretize the high-dimensional visual representations to bridge the gap with language models - a non-trivial challenge, as exis
arXiv:2607.22026v1 Announce Type: new Abstract: Detecting LLM-generated text remains challenging under zero-shot and training-free conditions, especially when detectors must generalize across datasets
arXiv:2607.22136v1 Announce Type: new Abstract: Empathetic Response Generation (ERG) requires models to recognize users' emotions and generate empathetic responses. Commonsense knowledge has been show
arXiv:2607.22514v1 Announce Type: new Abstract: Dysphagia is a debilitating late effect of head and neck cancer (HNC) treatment, yet timely identification of at-risk patients remains challenging in su
arXiv:2606.13842v2 Announce Type: replace Abstract: We present an algorithm for efficient domain-adaptive policy learning via kernel representations. Learning domain-adaptive policies is challenging s
arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written c
arXiv:2607.22287v1 Announce Type: new Abstract: Graph-based recommendations are widely adopted in real-world industrial applications. However, graphs in these systems often reach a massive scale, posi
arXiv:2601.17454v2 Announce Type: replace-cross Abstract: Centralized value learning underlies a broad class of multi-agent reinforcement learning methods, but its claimed advantage is typically evalu
arXiv:2607.22020v1 Announce Type: new Abstract: Learned manipulation policies increasingly predict motions for abstract 'hands' and are attractive in practice because they rely on easily collected dem
arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the nak
arXiv:2607.21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a
arXiv:2607.22004v1 Announce Type: new Abstract: Energy natural gradient descent (ENGD) aligns parameter updates with the curvature of an underlying function-space energy, but existing formulations ass
arXiv:2607.10428v2 Announce Type: replace Abstract: Evaluating large language models (LLMs) as multi-turn conversational partners requires probing capabilities that single-turn benchmarks miss: person
arXiv:2607.22039v1 Announce Type: new Abstract: Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language mo
arXiv:2607.17872v2 Announce Type: replace-cross Abstract: Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting ov
arXiv:2607.21685v1 Announce Type: new Abstract: A systematic review begins with someone reading thousands of abstracts to identify the few that are relevant, and classifiers are used to prioritise tha
arXiv:2607.22016v1 Announce Type: new Abstract: MEMEs are widely used on the internet and often carry strong elements of sarcasm or irony. Understanding their hidden meanings typically requires a join
arXiv:2607.22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoida
arXiv:2607.21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear f
arXiv:2607.22263v1 Announce Type: cross Abstract: A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed opti
arXiv:2607.22087v1 Announce Type: new Abstract: Generalization remains a critical bottleneck in AI-generated image detection. Because many modern generators are proprietary or adversarially modified,
arXiv:2607.21881v1 Announce Type: new Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accountin
arXiv:2506.18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subjec
arXiv:2607.21624v1 Announce Type: cross Abstract: Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks. On-device fine-tuning of transformer m
arXiv:2607.22205v1 Announce Type: new Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding. However, Earth observation applications req
arXiv:2607.22145v1 Announce Type: new Abstract: Open-source LiDAR-inertial odometry (LIO) systems have achieved remarkable benchmark accuracy, yet current state-of-the-art implementations are primaril