Knowledge catalogue
arXiv:2604.01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gai
arXiv:2608.09342v1 Announce Type: new Abstract: Video restoration methods exploit temporal information to recover information missing from degraded observations. However, reference frames within the s
arXiv:2608.09145v1 Announce Type: new Abstract: General-purpose multimodal large language models (MLLMs) are increasingly applied to infrared images, where they are commonly scored by answer accuracy
arXiv:2608.08957v1 Announce Type: new Abstract: Vehicle-mounted road cameras are vulnerable to motion blur, defocus, poor illumination, and noise, which can erase thin cracks and pothole boundaries ne
arXiv:2608.09778v1 Announce Type: new Abstract: Robotic manipulation requires perception systemsthat identify actionable parts such as handles, rims, triggers,and tool tips, not merely object categori
arXiv:2608.09112v1 Announce Type: new Abstract: This work presents a visual odometry (VO) system that leverages image edge features. Edges are spatially expressive cues commonly present across diverse
arXiv:2608.09891v1 Announce Type: new Abstract: Soft robotic systems are well suited for developing devices for biomedical applications. A bio-mimicking robotic soft esophagus (RoSE) is developed as a
arXiv:2511.09373v2 Announce Type: replace-cross Abstract: LLMs now tackle a wide range of software-related tasks, yet we show that their performance varies markedly both across and within these tasks.
arXiv:2608.07548v1 Announce Type: cross Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to make fine-grained navigation decisions under partial observabili
arXiv:2606.22790v2 Announce Type: replace-cross Abstract: In this paper, we investigate the tradeoffs between compute allocation and model performance for two speech processing tasks: Automatic Speech
arXiv:2608.07594v1 Announce Type: cross Abstract: Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods w
arXiv:2608.07923v1 Announce Type: new Abstract: Audio-visual event perception (AVEP) determines which events occur in a video, when they occur, and whether they are audible, visible, or both. Training
arXiv:2608.09568v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) aggregates token-level log-probability ratios via uniform summation, implicitly treating all tokens as contributing
arXiv:2608.07924v1 Announce Type: cross Abstract: Drifting models are a recent class of one-step generative models that evolve the model distribution during training using a predefined sample-based dr
arXiv:2608.09763v1 Announce Type: new Abstract: Muon's polar update is exact for an unweighted spectral geometry. We introduce GO-MUON, which uses a matched data-dependent geometry and reuses it acros
arXiv:2608.09049v1 Announce Type: new Abstract: Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are
arXiv:2410.01227v2 Announce Type: replace-cross Abstract: In the context of medical records, patients often experience testimonial injustice, where the textual account undermines the validity of their
arXiv:2608.07916v1 Announce Type: new Abstract: Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter array (CFA). Most
arXiv:2510.15968v2 Announce Type: replace-cross Abstract: Thermal management in 3D ICs is increasingly challenging due to higher power densities. Traditional PDE-solving-based methods, while accurate,
arXiv:2608.08685v1 Announce Type: new Abstract: Reliable semi-dense matching is essential for modern geometric vision systems. Designed under a coarse-to-fine paradigm, it achieves an optimal balance
arXiv:2601.19180v2 Announce Type: replace-cross Abstract: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines. However, existing approac
arXiv:2608.09271v1 Announce Type: cross Abstract: Group-based reinforcement learning objectives such as GRPO can allocate learning signal poorly across prompt difficulty: under binary rewards, group n
arXiv:2608.08984v1 Announce Type: new Abstract: Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but thresholding makes validation rankings t
arXiv:2608.09278v1 Announce Type: cross Abstract: GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity
arXiv:2603.22213v2 Announce Type: replace-cross Abstract: While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data
arXiv:2608.09887v1 Announce Type: new Abstract: Ball possession is the most-cited and most-misleading number in football: 60% recycled in one's own half is not 60% spent pinning the opponent back. Exi
arXiv:2608.08321v1 Announce Type: new Abstract: Floods and landslides often co-occur, but their relationships with environmental controls vary spatially. This study develops a spatial heterogeneity-aw
arXiv:2508.04224v2 Announce Type: replace Abstract: Reconstructing dynamic 3D scenes from monocular video remains fundamentally challenging due to the need to jointly infer motion, structure, and appe
arXiv:2608.07618v1 Announce Type: cross Abstract: Stochastic gradient descent for a loss function discontinuous across lower dimensional manifolds is analyzed by studying its differential equation lim
arXiv:2307.10053v5 Announce Type: replace-cross Abstract: In this paper, we focus on providing convergence guarantees for stochastic subgradient methods in minimizing nonsmooth nonconvex functions. We
arXiv:2608.09190v1 Announce Type: new Abstract: GK is a query-directed first-order prover that extends ordinary resolution-based proof search with explicit positive and negative claims, numerical conf
arXiv:2608.09767v1 Announce Type: new Abstract: Real-time MRI makes it possible to observe vocal-tract articulation during speech, but mapping these articulatory patterns to phonetic and phonological
arXiv:2608.08115v1 Announce Type: new Abstract: Point cloud completion commonly follows a coarse-to-fine paradigm, where a low-density coarse shape is first predicted and then upsampled to the target
arXiv:2608.08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hi
arXiv:2608.08103v1 Announce Type: new Abstract: Smooth acyclicity constraints answer whether a weighted support is a DAG, whereas structure learning asks which support change should be made. Existing
arXiv:2608.00417v2 Announce Type: replace Abstract: Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclu
arXiv:2608.09731v1 Announce Type: new Abstract: Wireless telerobotic manipulation relies on timely multi-view video feedback, but the available uplink bandwidth is often limited and dynamic. This pape
arXiv:2608.09264v1 Announce Type: new Abstract: Magnetic field strength is a major source of domain shift in magnetic resonance imaging (MRI), affecting signal-to-noise ratio, tissue contrast, spatial
arXiv:2608.09590v1 Announce Type: new Abstract: Learning reliable correspondences between images and point clouds is fundamental for 2D-3D matching. Despite recent progress in detection-free methods,
arXiv:2601.21747v4 Announce Type: replace-cross Abstract: Sepsis remains one of the most complex and heterogeneous syndromes in intensive care. While deep learning models achieve competitive performan
arXiv:2608.09351v1 Announce Type: cross Abstract: Test-time scaling improves LLM accuracy but multiplies inference cost, making the accuracy gained per unit of compute the metric that matters in deplo
arXiv:2608.09706v1 Announce Type: cross Abstract: Large language models can write parametric CAD programs from a natural-language description (text-to-CAD generation), but a single sample is often wro
arXiv:2507.10810v3 Announce Type: replace Abstract: We examined how social approval motivates online hate via the social approval theory, which argues social approval signals on hate messages predict
arXiv:2601.03100v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's ric
arXiv:2608.09093v1 Announce Type: cross Abstract: How a document's arrangement is written down, its notation, is a training variable that no dataset card records. The field has established that text-e
arXiv:2608.07566v1 Announce Type: new Abstract: We introduce a continuous metric field framework trained by a single causal contrastive loss. The framework encodes a scene into coefficients of a fixed
arXiv:2608.08317v1 Announce Type: new Abstract: Biological neural systems achieve high efficiency and robustness through compartmentalized architectures. In contrast, modern artificial neural networks
arXiv:2608.07737v1 Announce Type: new Abstract: This paper investigates whether the postmodern claim of unrestricted semantic indeterminacy, and its foundational Saussurean axiom of the arbitrary sign
arXiv:2606.23335v2 Announce Type: replace-cross Abstract: Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such
arXiv:2608.08020v1 Announce Type: new Abstract: Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shift
arXiv:2608.07713v1 Announce Type: new Abstract: Latent medical image generators usually treat the tokenizer as fixed preprocessing. We test whether this separation is valid in a controlled ChestMNIST
arXiv:2608.08139v1 Announce Type: new Abstract: Establishing the provenance of a language model---including its base checkpoint and possible overlap in training distributions---is a governance challen
arXiv:2608.08048v1 Announce Type: cross Abstract: This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine
arXiv:2608.08919v1 Announce Type: cross Abstract: During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resu
arXiv:2608.08446v1 Announce Type: new Abstract: Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concer
arXiv:2608.09558v1 Announce Type: new Abstract: How expressive is prompting a transformer? Answering this question is important for separating the roles of prompting, architecture, and pretraining in
arXiv:2608.07524v1 Announce Type: new Abstract: Training deep learning models on variable long sequences poses significant computational challenges. Existing methods force a difficult trade-off betwee
arXiv:2608.09464v1 Announce Type: cross Abstract: This paper studies hidden-target localization from range-bearing packets reported by a relay beacon whose global position and yaw are unknown. The veh
arXiv:2608.08554v1 Announce Type: cross Abstract: Power amplifier (PA) nonlinearity and memory effects significantly limit the spectral compliance, reliability, and energy efficiency of communication
arXiv:2505.15662v3 Announce Type: replace-cross Abstract: We introduce Transformer-based Neural Quantum Digital Twins (Tx-NQDTs) to reconstruct the low-energy spectral evolution of many-body quantum s