Knowledge catalogue
arXiv:2609.27043v1 Announce Type: new Abstract: Large language models have allowed the rapid deployment of pedagogical annotations corresponding to constructs of interest, allowing a natural language
arXiv:2609.27417v1 Announce Type: new Abstract: Symbiosis between humans and digital beings offers a vision for the future of human--machine interaction. In enduring human--machine relationships, pers
arXiv:2609.27264v1 Announce Type: new Abstract: We present GaussPDE, a framework that injects physically structured partial differential equation (PDE) dynamics into pretrained 3D Gaussian scenes with
arXiv:2609.28179v1 Announce Type: new Abstract: GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeate
arXiv:2609.27695v1 Announce Type: new Abstract: Perceiving objects in the environment is a fundamental capability of autonomous robots. In dark or low-light environments, external cameras often fail t
arXiv:2609.27893v1 Announce Type: cross Abstract: A blind image denoising framework based on an infinite directional lower semi-frame (DLSF) is proposed for additive white Gaussian noise. The model em
arXiv:2609.27493v1 Announce Type: new Abstract: Under the AI Flow framework, communication networks distribute intelligence across devices, edge servers, and clouds, and computation at the receiver be
arXiv:2609.27216v1 Announce Type: cross Abstract: Topology optimization (TO) remains computationally intensive due to repeated finite element analysis (FEA) evaluations required at each iteration. Whi
arXiv:2609.27003v1 Announce Type: new Abstract: Runway walking requires coordinated control of posture, stride, foot placement, and whole-body motion to effectively present clothing and convey a disti
arXiv:2609.27728v1 Announce Type: new Abstract: We introduce NeuralSRNF, a novel framework for the statistical shape analysis and generation of genus-zero 3D and 4D objects that undergo nonrigid defor
arXiv:2609.26979v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a flexible framework for learning unknown functions from noisy measurements while quantifying predictive uncertainty, m
arXiv:2609.27290v1 Announce Type: new Abstract: Short-horizon forecasts of atmospheric temperature are needed to support climate-aware digital-twin systems, but such forecasts must be produced where t
arXiv:2609.27588v1 Announce Type: cross Abstract: Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deploye
arXiv:2609.26783v1 Announce Type: cross Abstract: We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework com
arXiv:2609.26781v1 Announce Type: new Abstract: A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent syste
arXiv:2609.26490v1 Announce Type: new Abstract: This study introduces an interdisciplinary framework for benchmarking robots deployed in public environments, addressing the gap between traditional lab
arXiv:2609.26399v1 Announce Type: new Abstract: Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning di
arXiv:2609.26426v1 Announce Type: new Abstract: Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution
arXiv:2606.22886v2 Announce Type: replace Abstract: Objective: This study aims to improve the reliability and robustness of medical named entity recognition (NER) in Chinese atopic dermatitis (AD) cli
arXiv:2609.22719v1 Announce Type: cross Abstract: Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment
arXiv:2609.25966v1 Announce Type: new Abstract: This paper introduces GRIP, a pose-conditioned refinement framework for pixel-to-point matching and 2D to 3D registration. Given an initial coarse pose
arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation u
arXiv:2609.25697v1 Announce Type: new Abstract: Purpose: To develop and evaluate an interpretable artificial intelligence (AI) framework for glaucoma screening from low-cost portable, handheld retinal
arXiv:2609.22529v1 Announce Type: new Abstract: International law provides the normative framework through which states coordinate action, regulate armed conflict, and protect human rights, yet its te
arXiv:2609.26634v1 Announce Type: new Abstract: We introduce Knowledge Pull Requests (KPRs), a framework for continual document authoring that makes each change interpretable. Documents require ongoin
arXiv:2609.23058v1 Announce Type: new Abstract: Current agent runtimes that plan before acting generally execute a step once it becomes ready. We present LazyAgent, a unified execution framework for a
arXiv:2509.05500v2 Announce Type: replace Abstract: Autonomous microrobots could enable minimally invasive interventions in confined biological environments, but their operation requires real-time nav
arXiv:2509.20634v3 Announce Type: replace-cross Abstract: We provide a new framework for estimating peer effects when outcomes are multivariate behavioral measures derived from written text using an L
arXiv:2510.11058v2 Announce Type: replace Abstract: Photoplethysmography (PPG) is widely used in wearable health monitoring, but its reliability is often degraded by noise and motion artifacts, limiti
arXiv:2603.29529v2 Announce Type: replace-cross Abstract: Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample
arXiv:2609.25917v1 Announce Type: new Abstract: In this work, we propose a communication-free framework for vision-based formation control of fully actuated underwater robots subject to sensing constr
arXiv:2609.24260v1 Announce Type: cross Abstract: We study the problem of adversarially robust PAC learning. In this framework, the learner observes independent samples from an unknown distribution ov
arXiv:2609.24737v1 Announce Type: new Abstract: We introduce Ananke, a representation-learning framework that scaffolds latent representations onto a structured product-torus prior, and its flagship v
arXiv:2609.24629v1 Announce Type: new Abstract: A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from m
arXiv:2609.22162v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation i
arXiv:2609.24141v1 Announce Type: new Abstract: On-policy distillation (OPD) pays twice for each fresh batch: the student generates trajectories and a stronger teacher scores them. Existing methods im
arXiv:2609.22360v1 Announce Type: new Abstract: Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financ
arXiv:2411.05005v2 Announce Type: replace Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most exi
arXiv:2609.24668v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep L
arXiv:2609.24250v1 Announce Type: new Abstract: The shipping industry has a significant impact on the global economy, emphasizing the need for operational availability and safety through the use of ef
arXiv:2609.24627v1 Announce Type: new Abstract: Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and d
arXiv:2609.23703v1 Announce Type: new Abstract: Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated
arXiv:2609.22538v1 Announce Type: new Abstract: Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does no
arXiv:2609.24083v1 Announce Type: new Abstract: Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than su
**Genie One MCP: Give any AI Agent the Right Business Context** The guide introduces Genie One MCP as a framework for embedding relevant business context in artificial intelligence agents, enabling th
arXiv:2509.05259v2 Announce Type: replace Abstract: Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete relianc
arXiv:2510.26915v2 Announce Type: replace Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large langua
arXiv:2609.22351v1 Announce Type: new Abstract: Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a rob
arXiv:2503.24111v4 Announce Type: replace-cross Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical
arXiv:2609.23915v1 Announce Type: cross Abstract: This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata fro
arXiv:2609.24145v1 Announce Type: new Abstract: We present MimicAgent, a prompt-to-trajectory generation framework for learning dynamic quadruped skills. Although reward shaping is extensively used wh
arXiv:2509.00754v3 Announce Type: replace Abstract: Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends on
arXiv:2609.22687v1 Announce Type: new Abstract: We present PanoSeg3R, a feed-forward framework for 3D panoramic semantic segmentation. Unlike existing methods designed for perspective inputs, PanoSeg3
arXiv:2609.22171v1 Announce Type: new Abstract: Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. Howeve
arXiv:2512.09201v2 Announce Type: replace-cross Abstract: We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persist
arXiv:2609.22678v1 Announce Type: cross Abstract: Density-Driven Optimal Control (D2OC) provides an effective framework for steering multi-agent systems toward prescribed spatial distributions. Howeve
arXiv:2609.23280v1 Announce Type: new Abstract: Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compa
arXiv:2609.22576v1 Announce Type: cross Abstract: Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neuron
arXiv:2609.23263v1 Announce Type: new Abstract: Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode