Knowledge catalogue
arXiv:2512.05136v3 Announce Type: replace-cross Abstract: CAD remains a major global public health burden, yet scalable screening tools are limited. Although CCTA is a first-line non-invasive diagnost
arXiv:2508.20836v3 Announce Type: replace Abstract: In this letter, we report the first experimental demonstration of the recently emerged new paradigm in hovering and flapping flight physics called (
arXiv:2512.23070v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models enable scalable neural networks through conditional computation, offering enhanced effectiveness and efficiency for
arXiv:2605.16348v1 Announce Type: cross Abstract: Training-free guidance enables pre-trained diffusion and flow models to optimize application-specific objectives using feedback from external black-bo
arXiv:2605.18472v1 Announce Type: cross Abstract: Generative models can produce nonsensical text, unrealistic images, and unstable materials faster than simulation or human review can absorb; without
arXiv:2505.16786v2 Announce Type: replace Abstract: We introduce FlowMixer, a single-layer neural architecture that leverages constrained matrix operations to model structured spatiotemporal patterns
arXiv:2605.18155v1 Announce Type: new Abstract: Translating formal language into natural language is a foundational challenge in NLP, driving various downstream applications in semantic parsing, theor
arXiv:2605.18607v1 Announce Type: new Abstract: Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which
arXiv:2605.16319v1 Announce Type: new Abstract: Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomar
arXiv:2601.06163v2 Announce Type: replace Abstract: The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or
arXiv:2605.16594v1 Announce Type: cross Abstract: In this paper, we develop a physics-informed deep operator learning framework for solving multi-term time-fractional mixed diffusion-wave equations (T
arXiv:2605.16366v1 Announce Type: cross Abstract: Video MLLMs face a persistent tension between spatial fidelity and temporal coverage: preserving fine-grained visual details requires many spatial tok
arXiv:2605.18462v1 Announce Type: new Abstract: Named entity recognition (NER) has been one of the essential preliminary steps in modern NLP applications. This report focuses on implementing the NER t
arXiv:2603.17588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently.
arXiv:2605.16928v1 Announce Type: cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely ei
arXiv:2605.17968v1 Announce Type: new Abstract: We study the approximation of nonlinear operators between function spaces by transformers. Our approach is to lift functions to measures supported on th
arXiv:2605.18010v1 Announce Type: new Abstract: Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite str
arXiv:2605.18252v1 Announce Type: new Abstract: We introduce GaussianZoom, a generative zoom-in 3D reconstruction system with an iterative progressive framework that combines geometry-consistent scene
arXiv:2605.17456v1 Announce Type: cross Abstract: Multiple instance learning (MIL) is the standard approach for whole-slide image (WSI) classification and survival prediction, where attention-based mo
arXiv:2405.06415v2 Announce Type: replace-cross Abstract: While metric and similarity learning has been extensively studied from several theoretical perspectives, a rigorous understanding of its gener
arXiv:2605.18398v1 Announce Type: cross Abstract: We develop a complete theory of projective cross-ratios in n-dimensional Plane-Based Geometric Algebra (PGA), R(n,0,1), covering geometric objects of
arXiv:2605.18422v1 Announce Type: cross Abstract: The functional ANOVA, or Hoeffding decomposition, provides a principled framework for interpretability by decomposing a model prediction into main eff
arXiv:2605.16355v1 Announce Type: cross Abstract: We present Density-Sampled Gaussians (DeG), a novel 3D representation designed to bridge the gap between adaptive rendering primitives and scalable ge
arXiv:2605.16277v1 Announce Type: cross Abstract: This mid-year report summarizes teacher use of Colleague AI across 12 Washington State school districts from September 1 to December 31, 2025. Produce
arXiv:2605.18365v1 Announce Type: new Abstract: Generating geometrically consistent videos remains an open challenge: text-to-video diffusion models trained on web-scale data treat geometry only impli
arXiv:2605.17354v1 Announce Type: new Abstract: Monocular 3D hand reconstruction is intrinsically a geometric problem, yet RGB appearance features alone often struggle to resolve severe ambiguities ca
arXiv:2605.18276v1 Announce Type: cross Abstract: Learning dynamical systems through operator-theoretic representations provides a powerful framework for analyzing complex dynamics, as spectral quanti
arXiv:2505.20914v2 Announce Type: replace Abstract: General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simul
arXiv:2602.08206v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation has become an important direction in remote sensing, as it enables recognition beyond predefined land-cover ca
arXiv:2605.16371v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thou
arXiv:2602.23058v2 Announce Type: replace Abstract: Energy-based predictive world models provide a powerful approach for multi-step visual planning by reasoning over latent energy landscapes rather th
arXiv:2506.13189v3 Announce Type: replace-cross Abstract: Robot teleoperation via augmented reality (AR) offers a promising path toward more intuitive human-robot interaction (HRI). We present a head-
arXiv:2605.17086v1 Announce Type: cross Abstract: Automation affects the labour content of work differently across different contexts. Yet, most existing exposure measures assign fixed scores to tasks
arXiv:2605.16520v1 Announce Type: new Abstract: Sampling-based optimization (SBO), like cross-entropy method and evolutionary algorithms, has achieved many successes in solving non-convex problems wit
arXiv:2506.12648v2 Announce Type: replace-cross Abstract: Iteration complexities for optimizing smooth functions with first-order algorithms are typically stated in terms of a global Lipschitz constan
arXiv:2512.22473v5 Announce Type: replace-cross Abstract: Transformers empirically perform precise probabilistic reasoning in carefully constructed ``Bayesian wind tunnels'' and in large-scale languag
arXiv:2602.07884v2 Announce Type: replace-cross Abstract: Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models offer interpretabi
arXiv:2511.19078v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, including multi-step
arXiv:2605.17727v1 Announce Type: new Abstract: Frozen vision-language embeddings contain signals at multiple semantic resolutions, from object identity to attributes, relations, and full-caption mean
arXiv:2605.16668v1 Announce Type: cross Abstract: We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces
arXiv:2605.16714v1 Announce Type: new Abstract: Security knowledge graphs can provide computable external memory for security agents, but constructing them from long-form cyber threat intelligence (CT
arXiv:2605.16873v1 Announce Type: new Abstract: Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at
arXiv:2605.16389v1 Announce Type: cross Abstract: Haptic rendering of viscoelastic materials that exhibit creep and stress relaxation is crucial for many applications, such as medical training with re
On Monday, the jury in Musk v. Altman dealt Elon Musk a major blow—reaching a unanimous advisory verdict that he had sued OpenAI too late and, as a result, his claims are barred by the applicable stat
arXiv:2605.17875v1 Announce Type: new Abstract: The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. Howeve
arXiv:2605.16442v1 Announce Type: cross Abstract: Long-horizon vessel trajectory forecasting under real ocean conditions is critical for collision avoidance, traffic management, and route planning. Ho
arXiv:2605.17177v1 Announce Type: cross Abstract: Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diagonal linear
arXiv:2605.18408v1 Announce Type: new Abstract: Accurate vessel estimated-time-of-arrival forecasts are critical for port operations and decarbonization, yet global-scale travel-time prediction remain
arXiv:2605.17543v1 Announce Type: new Abstract: Video outpainting generates plausible visual content beyond the original spatial extent of a video, playing a key role in adapting videos to diverse dis
arXiv:2605.16953v1 Announce Type: new Abstract: While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled b
How do we design materials with AI? Excited to introduce Crys-JEPA, a new generative technique in collaboration w/ @liun_online, Kostya Novoselov, @ylecun & team. We achieved 47.9% VSUN on MP20 by bui
Physics-informed neural networks (PINNs) use specialized loss functions that combine data-fitting terms with physics constraints derived from differential equations, allowing the network to learn solu
arXiv:2603.02512v4 Announce Type: replace-cross Abstract: Human-Certified Module Repositories (HCMRs) are introduced in this work as a new architectural model for constructing trustworthy software in
arXiv:2605.18345v1 Announce Type: cross Abstract: Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale qua
arXiv:2605.17286v1 Announce Type: new Abstract: While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, g
arXiv:2605.16682v1 Announce Type: new Abstract: Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial a
arXiv:2605.17248v1 Announce Type: new Abstract: Diffusion-based extit{image-to-video} (I2V) generation has become a central direction in generative models by turning a reference image, with optional c
arXiv:2605.16390v1 Announce Type: new Abstract: We investigate whether the training protocol can induce spatial locality in the early layers of a Vision Transformer (ViT) trained from scratch, without
arXiv:2602.18217v2 Announce Type: replace Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate f
arXiv:2605.18467v1 Announce Type: new Abstract: Recent diffusion-based methods have achieved impressive progress in video content manipulation. However, they typically ignore the accompanying audio, l