Knowledge catalogue
arXiv:2609.21870v1 Announce Type: cross Abstract: Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and me
arXiv:2609.21707v1 Announce Type: new Abstract: We present NeuRIO, a streaming neural estimator for anchor-free 6-DoF relative inertial odometry using only identified inter-robot bearings, ranges, and
arXiv:2609.21114v1 Announce Type: new Abstract: Robots carrying out tasks in dark environments need to localize from a single RGB camera, in light so low that the per-pixel signal approaches the senso
arXiv:2603.29715v2 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns an
arXiv:2609.21179v1 Announce Type: new Abstract: Neural morphological generation systems often achieve high aggregate accuracy on benchmark datasets, yet such performance can conceal systematic errors
arXiv:2609.21822v1 Announce Type: new Abstract: While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limi
arXiv:2604.00200v2 Announce Type: replace Abstract: We study offline constrained reinforcement learning from human feedback with multiple preference oracles. Motivated by applications that trade off p
arXiv:2609.21390v1 Announce Type: new Abstract: Air operations rely on complex rules, established procedures, and time-critical analysis under limited connectivity and strict security constraints. In
arXiv:2609.21392v1 Announce Type: new Abstract: Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather
arXiv:2609.22069v1 Announce Type: new Abstract: Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omn
arXiv:2609.21465v1 Announce Type: cross Abstract: We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly
arXiv:2609.21561v1 Announce Type: cross Abstract: On-policy self-distillation provides dense, token-level supervision by conditioning a model on privileged information and distilling the resulting tea
arXiv:2605.11986v2 Announce Type: replace Abstract: This article analyzes the use of Large Language Models (LLMs) as support for the conceptual modeling of relational databases through the automatic g
arXiv:2609.21001v1 Announce Type: new Abstract: Substantial efforts have been devoted to making deep learning objectives, representations, and architectures interpretable, with the goal of improving t
arXiv:2609.21626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized different
arXiv:2609.21550v1 Announce Type: cross Abstract: Auto-bidding is central to computational advertising, where strategies must maximize advertisers' conversion value under economic constraints. It has
arXiv:2609.21199v1 Announce Type: new Abstract: Manga is a comic drawn by black and white paints gaining popularity around the world. Onomatopoeia in Manga specifically appeals to the audience with it
arXiv:2609.21527v1 Announce Type: new Abstract: Graph-enhanced multi-agent systems (G-MAS) coordinate large language model agents through communication graphs and role assignments, which determine how
arXiv:2609.21178v1 Announce Type: new Abstract: Service applications for human-robot interaction are commonly written against the hardware-specific interfaces of one platform, so a change of hardware
arXiv:2609.21480v1 Announce Type: new Abstract: Omnidirectional video saliency prediction plays an important role in many immersive multimedia applications, including viewport-adaptive streaming and c
arXiv:2609.21445v1 Announce Type: new Abstract: We prove that the optimal expected mistake bound of online learning a function class H by a randomized proper learning algorithm is O(mathtt{L}(H) log T
arXiv:2609.21693v1 Announce Type: new Abstract: Equivalent finite parameterizations can represent the same functions and intrinsic norm yet induce different optimization algorithms. We study this effe
arXiv:2609.21659v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies solve the same manipulation task through different action interfaces, but task success alone does not establish
arXiv:2502.02542v5 Announce Type: replace Abstract: A reasoning language model (RLM) generates costly reasoning tokens, often hidden from the users, that help it excel at many tasks. Our Overthink att
arXiv:2609.21424v1 Announce Type: new Abstract: Few-shot semantic segmentation (FSS) of strip steel surface defects (S^3D) has posed significant challenges distinct from natural scenes. Unlike natural
arXiv:2609.22053v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned
arXiv:2609.21789v1 Announce Type: new Abstract: Most multilingual dysarthria-severity systems either train on a single aetiology-language pair or pool heterogeneous aetiologies into one label space. W
arXiv:2609.21590v1 Announce Type: cross Abstract: Accurate prediction of unsteady aerodynamic loads remains a major challenge in turbomachinery design. High-fidelity Computational Fluid Dynamics (CFD)
arXiv:2609.21054v1 Announce Type: cross Abstract: Image diffusion models have shown impressive image generation capabilities but are often hard to control, in contrast to classical computer graphics p
arXiv:2609.20836v1 Announce Type: new Abstract: Physiological signals support diverse clinical and monitoring tasks, yet existing physiological signal foundation models typically require task-specific
arXiv:2609.20983v1 Announce Type: new Abstract: Terrain assessment is a critical capability for off-road mobile robots, enabling safe and reliable navigation through unstructured and geometrically com
arXiv:2609.21263v1 Announce Type: new Abstract: Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optimi
arXiv:2609.21059v1 Announce Type: cross Abstract: Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent adva
arXiv:2609.21780v1 Announce Type: new Abstract: 3D object detection from LiDAR point clouds faces a fundamental dilemma: voxel-based methods achieve efficiency at the cost of geometric quantization, w
arXiv:2609.21493v1 Announce Type: new Abstract: Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whet
arXiv:2609.21838v1 Announce Type: new Abstract: Social-navigation algorithms are often evaluated under a fixed pedestrian-behavior distribution, despite substantial variation in pedestrian responses t
arXiv:2609.21609v1 Announce Type: cross Abstract: Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the
arXiv:2609.22056v1 Announce Type: cross Abstract: Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two resul
arXiv:2609.21383v1 Announce Type: new Abstract: Depth-recurrent language models refine predictions through repeated latent updates. Why can intermediate answers agree with the endpoint while their sco
arXiv:2609.21583v1 Announce Type: cross Abstract: Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redunda
arXiv:2609.22040v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--pla
arXiv:2609.21673v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural s
arXiv:2609.21351v1 Announce Type: new Abstract: Table extraction suffers from frequent structural errors and semantic hallucinations. We propose PrismAlign, a multi-VLM framework aligning diverse visu
arXiv:2402.04013v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inve
arXiv:2510.24233v2 Announce Type: replace Abstract: Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or pr
arXiv:2609.21382v1 Announce Type: new Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability
arXiv:2609.21264v1 Announce Type: cross Abstract: Spatial NPUs such as AMD XDNA place compute tiles beside small local memories and leave data movement between them to software. Mapping a multi-stage
arXiv:2609.21005v1 Announce Type: new Abstract: The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for
arXiv:2609.21369v1 Announce Type: cross Abstract: This paper presents a comprehensive framework for robot manipulation failure analysis that includes binary failure detection, failure categorization,
arXiv:2510.18082v3 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits i
arXiv:2609.21462v1 Announce Type: new Abstract: Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity underst
arXiv:2609.21753v1 Announce Type: new Abstract: Contact-rich manipulation requires policies to generate precise actions by reasoning over contact forces, robot configurations, and interaction historie
arXiv:2609.21541v1 Announce Type: new Abstract: Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face t
arXiv:2609.21407v1 Announce Type: new Abstract: Quantization offers a practical path to deploying diffusion models with reduced memory and computation, but aggressive compression can cause quantized o
arXiv:2609.22038v1 Announce Type: new Abstract: We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Quran
arXiv:2609.21953v1 Announce Type: new Abstract: Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this probl
arXiv:2609.21690v1 Announce Type: new Abstract: A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the contro
arXiv:2609.20971v1 Announce Type: new Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation
arXiv:2609.20847v1 Announce Type: new Abstract: Anxiety is among the most common mental health conditions, and people often write about it online well before seeking clinical help. Practitioners build
arXiv:2609.20845v1 Announce Type: new Abstract: A decoder that turns video or audio into text conventionally consumes the entire input before emitting a word. Offline this is merely more than the task