Search: “framework”

arXiv:2605.29329v1 Announce Type: cross Abstract: A novel two-phase molecule inference framework, mol-infer, has recently been developed to infer chemical graphs with prescribed abstract structures an

arXiv:2605.29685v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly applied in social contexts such as emotional companionship and customer service, measuring their social

arXiv:2508.02537v3 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful framework for solving PDEs by embedding physical laws into the learning process. However,

arXiv:2605.29366v1 Announce Type: new Abstract: Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed b

arXiv:2508.14610v2 Announce Type: replace Abstract: Despite extensive developments in motion planning of autonomous aerial vehicles (AAVs), existing frameworks faces the challenges of local minima and

arXiv:2603.20980v2 Announce Type: replace-cross Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the

arXiv:2605.28273v1 Announce Type: new Abstract: The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy

arXiv:2503.11477v2 Announce Type: replace Abstract: Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted inte

arXiv:2605.28625v1 Announce Type: new Abstract: Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussi

arXiv:2605.26631v1 Announce Type: cross Abstract: We propose KO-PDE-IDENT, a data-driven framework for identifying parsimonious partial differential equations (PDEs) with false discovery rate (FDR) co

arXiv:2512.21208v3 Announce Type: replace Abstract: We develop a finite-dimensional sensitivity framework for studying stability in learning systems whose states include representations, parameters, a

arXiv:2508.03526v2 Announce Type: replace Abstract: One central goal of robotics is to enable robots to interact with the physical world. Traditional manipulation studies primarily focus on single rob

arXiv:2605.24663v1 Announce Type: cross Abstract: This paper presents CyBOKClaw, an interpretable human-in-the-loop retrieval framework for mapping cybersecurity keywords or phrases (KWoPs) to the Cyb

arXiv:2605.24495v1 Announce Type: new Abstract: This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby

arXiv:2605.24592v1 Announce Type: new Abstract: This paper presents MuGen, a data-driven framework for learning and deploying multi-skill locomotion on humanoid robots. MuGen enables a robot to perfor

arXiv:2503.17599v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated considerable potential in general practice. However, existing benchmarks and evaluation frameworks pr

arXiv:2605.22552v1 Announce Type: new Abstract: Fashion image retrieval is a cornerstone of modern e-commerce systems. A unified framework that supports diverse query formats and search intentions is

arXiv:2602.03205v2 Announce Type: replace Abstract: While current humanoid whole-body control frameworks predominantly rely on the static environment assumptions, addressing tasks characterized by hig

arXiv:2605.21258v1 Announce Type: new Abstract: Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either

arXiv:2601.00418v2 Announce Type: replace-cross Abstract: We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for se

arXiv:2605.20635v1 Announce Type: new Abstract: This paper proposes a general machine learning framework called the localization method, which is fundamentally built on two core concepts: localization

arXiv:2605.19420v1 Announce Type: new Abstract: Grounding open-ended semantic instructions into physically executable local goals is a fundamental challenge in human-robot interaction. While existing

arXiv:2605.19881v1 Announce Type: new Abstract: We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autono

arXiv:2511.12710v2 Announce Type: replace Abstract: Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet many still formulate attack optimizati

arXiv:2603.20873v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with privat

arXiv:2605.18128v1 Announce Type: new Abstract: Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence model

arXiv:2511.02610v2 Announce Type: replace Abstract: The development of smart systems (i.e., systems enhanced with AI components) has thrived thanks to the rapid advancements in neural networks (NNs).

arXiv:2605.15999v1 Announce Type: new Abstract: This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We

arXiv:2605.15845v1 Announce Type: new Abstract: This paper presents a novel framework for Jacobian computation in motion optimization problems involving multi-link systems, where physical quantities a

arXiv:2605.14440v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods s

arXiv:2605.13794v1 Announce Type: cross Abstract: We present BlitzGS, a distributed 3DGS framework that reduces active Gaussian workload for fast city-scale reconstruction. BlitzGS manages this worklo

arXiv:2605.13415v1 Announce Type: cross Abstract: This paper presents a multi-stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identi

arXiv:2604.00001v2 Announce Type: replace-cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but exist

arXiv:2605.13632v1 Announce Type: cross Abstract: In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied

arXiv:2605.12536v1 Announce Type: cross Abstract: The Free Energy Principle (FEP) is a leading framework for mathematically modeling self-organization and learning, while Integrated Information Theory

arXiv:2507.10797v2 Announce Type: replace Abstract: This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed ban

arXiv:2602.07668v2 Announce Type: replace Abstract: The looking-in-looking-out (LILO) framework has enabled intelligent vehicle applications that understand both the outside scene and the driver state

arXiv:2605.11284v1 Announce Type: cross Abstract: Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded

arXiv:2605.08506v1 Announce Type: new Abstract: Robust optimization (RO) provides a principled framework for decision-making under uncertainty, but its performance critically depends on the choice of

arXiv:2605.09724v1 Announce Type: new Abstract: Existing accounts of grokking explain the phenomena in terms of mechanistic frameworks such as circuit efficiency or lazy-to-rich transitions. However,

arXiv:2605.10851v1 Announce Type: new Abstract: We introduce the Generalized Turing Test (GTT), a formal framework for comparing the capabilities of arbitrary agents via indistinguishability. For agen

arXiv:2605.09396v1 Announce Type: cross Abstract: This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate nois

arXiv:2512.09629v2 Announce Type: replace Abstract: We present an end-to-end framework for planning supported by verifiers. An orchestrator receives a human specification written in natural language a

arXiv:2605.07357v1 Announce Type: new Abstract: Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, ex

arXiv:2605.05095v1 Announce Type: cross Abstract: We develop a framework for task-specific active next-best-view selection in 3D reconstruction from point clouds, by casting the problem in the languag

arXiv:2605.02911v1 Announce Type: new Abstract: Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts.