Search: “framework”

arXiv:2604.26963v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic w

arXiv:2604.27300v1 Announce Type: new Abstract: Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficien

arXiv:2604.27733v1 Announce Type: new Abstract: Preference learning has become the foundation of aligning Large Language Models (LLMs) with human intent. Popular methods, such as Direct Preference Opt

arXiv:2510.23498v2 Announce Type: replace-cross Abstract: Exploiting low-precision computations has become a standard strategy in deep learning to address the growing computational costs imposed by ev

arXiv:2604.27934v1 Announce Type: new Abstract: Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting sign

arXiv:2512.10881v2 Announce Type: replace Abstract: Motion capture now underpins content creation far beyond digital humans, yet most existing pipelines remain species- or template-specific. We formal

arXiv:2604.28130v1 Announce Type: new Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts joint pos

arXiv:2407.08668v3 Announce Type: replace-cross Abstract: In this work, we propose a simulation-based estimation approach using generative neural networks to determine dependencies of precipitation ma

arXiv:2604.27654v1 Announce Type: new Abstract: Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,an

arXiv:2512.10941v2 Announce Type: replace-cross Abstract: Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey

arXiv:2604.26979v1 Announce Type: cross Abstract: In-memory computing (IMC) enables energy-efficient neural network inference by computing analog matrix-vector multiplications (MVM) in memory crossbar

arXiv:2510.18183v2 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent

arXiv:2604.28107v1 Announce Type: new Abstract: Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracki

arXiv:2604.27889v1 Announce Type: new Abstract: Semantic segmentation and change detection are two fundamental challenges in remote sensing, requiring models to capture either spatial semantics or tem

arXiv:2604.26970v1 Announce Type: cross Abstract: Knowledge graphs used for retrieval treat all facts as equally current. Existing temporal approaches apply uniform decay, using a single forgetting cu

arXiv:2512.14044v3 Announce Type: replace-cross Abstract: The deployment of Vision-Language Models (VLMs) in safety-critical domains like autonomous driving (AD) is critically hindered by reliability

arXiv:2604.28147v1 Announce Type: new Abstract: Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a uni

arXiv:2604.27599v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candi

arXiv:2602.13559v2 Announce Type: replace Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Convention

arXiv:2412.16720v2 Announce Type: replace Abstract: The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provi

arXiv:2604.27150v1 Announce Type: new Abstract: Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested

arXiv:2506.22500v2 Announce Type: replace-cross Abstract: Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs

arXiv:2506.08332v3 Announce Type: replace Abstract: Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In integrated circuit design, modern flows

arXiv:2604.27118v1 Announce Type: cross Abstract: We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomo

arXiv:2604.27870v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) are widely assumed to be translation-invariant, yet standard architectures exhibit a startling fragility: even a si

arXiv:2604.26991v1 Announce Type: cross Abstract: Recent advances in data-centric medical AI have produced highly accurate diagnostic systems, but the emphasis on data curation and performance metrics

arXiv:2604.28169v1 Announce Type: cross Abstract: Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic reb

arXiv:2601.11908v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execut

arXiv:2604.26968v1 Announce Type: cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving. Current

arXiv:2604.28055v1 Announce Type: cross Abstract: Individualized Alzheimer's disease (AD) progression prediction requires models that use irregular visits, account for censoring, avoid diagnostic leak

arXiv:2604.27106v1 Announce Type: cross Abstract: Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward

arXiv:2604.27323v1 Announce Type: cross Abstract: Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their eff

arXiv:2604.28025v1 Announce Type: new Abstract: Single-image human mesh recovery provides a compact 3D, person-centric representation that supports analysis, animation, AR and VR, rehabilitation, and

arXiv:2604.27859v1 Announce Type: new Abstract: Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined env

arXiv:2604.27504v1 Announce Type: new Abstract: Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision a

arXiv:2604.27559v1 Announce Type: cross Abstract: Radiology report generation (RRG) has emerged as a promising approach to alleviate radiologists' workload and reduce human errors by automatically gen

arXiv:2604.26988v1 Announce Type: new Abstract: Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unfores

arXiv:2604.28161v1 Announce Type: new Abstract: The robotic manipulation of Deformable Linear Objects (DLOs) is a fundamental challenge due to the high-dimensional, non-linear dynamics of flexible str

arXiv:2604.27780v1 Announce Type: cross Abstract: Large Language Models (LLMs) have rapidly improved in performance across code-related tasks, making their integration into Register Transfer Level (RT

arXiv:2604.27447v1 Announce Type: cross Abstract: Modern stochastic optimization pipelines increasingly rely on learned generative models to represent uncertainty, while downstream decisions are evalu

arXiv:2604.27508v1 Announce Type: new Abstract: Understanding human actions is critical for advancing behavior analysis in human-robot interaction. Particularly in tasks that demand quick and proactiv

arXiv:2604.27025v1 Announce Type: cross Abstract: Automatic feature engineering is an effective approach for improving predictive performance in tabular learning. However, expand-and-reduce methods, s

arXiv:2604.28163v1 Announce Type: cross Abstract: The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in

arXiv:2604.27620v1 Announce Type: new Abstract: Vision-and-Language Navigation (VLN) aims to enable an embodied agent to follow natural-language instructions and navigate to a target location in unsee

arXiv:2604.27326v1 Announce Type: cross Abstract: Hyperspectral image super-resolution is essential for enhancing the spatial fidelity of HSI data, yet existing deep learning methods often struggle wi

arXiv:2604.27329v1 Announce Type: cross Abstract: 3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts -- critical for efficient editing and modeling. E