Knowledge catalogue
arXiv:2607.01698v2 Announce Type: replace Abstract: 3D Gaussian Splatting has demonstrated remarkable potential in novel view synthesis. In contrast to small-scale scenes, large-scale scenes inevitabl
arXiv:2607.03999v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existi
arXiv:2607.04851v1 Announce Type: cross Abstract: Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source doma
arXiv:2607.03881v1 Announce Type: cross Abstract: Codon harmonization aims to adapt the coding sequences for heterologous expression while preserving the native-like patterns of frequent and rare codo
arXiv:2607.03765v1 Announce Type: new Abstract: 3D reconstruction from sparse views is a challenging task in 3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS) have achieved remarkable
arXiv:2607.04457v1 Announce Type: new Abstract: Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessm
arXiv:2602.17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known clas
arXiv:2607.03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heteroge
arXiv:2607.02922v1 Announce Type: new Abstract: Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense sp
Yann LeCun shared details about an early AI prototype project developed over 9 months by a small team in Paris at UMA_Rob, covering artificial intelligence, software, and hardware components. The proj
arXiv:2607.03329v1 Announce Type: new Abstract: Conventional uniform convergence bounds and empirical risk minimization break down in massive over-parameterized models, such as large language transfor
arXiv:2607.05250v1 Announce Type: new Abstract: Neural speech codecs are increasingly used as intermediate representations in codec-based speech generation systems. TiCodec introduces a factorized rep
arXiv:2501.00048v2 Announce Type: replace-cross Abstract: Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes.
arXiv:2607.04333v1 Announce Type: new Abstract: Grokking -- generalization arriving long after training-set interpolation -- can be accelerated by structure-agnostic interventions: gradient filtering,
arXiv:2607.04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as doc
Super-fun in-depth discussion 🙏🙏🙏 World models, JEPA vs generative architectures, anti-collapse methods for JEPA SSL including contrastive, distillation, and information maximization methods, an expla
arXiv:2607.02524v1 Announce Type: cross Abstract: Accurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to
arXiv:2607.02573v1 Announce Type: new Abstract: Islamic geometric patterns are governed by exact rotational symmetry and strict construction rules. This paper treats these rules as formal geometric kn
arXiv:2607.05392v1 Announce Type: new Abstract: We present SynCity 3000, a framework for generating 3D scenes that are globally coherent while enabling fine-grained layout control. Building on the abi
arXiv:2607.04848v1 Announce Type: cross Abstract: While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing e
arXiv:2607.04091v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable performance in graph representation learning, yet their inherent vulnerability to adversarial atta
arXiv:2607.03390v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has revolutionized novel-view synthesis with its fast and high-fidelity rendering. However, rendering at high FPS and low l
arXiv:2603.24967v2 Announce Type: replace Abstract: Understanding why a large language model (LLM) is uncertain about the response is important for their reliable deployment. Current approaches, which
arXiv:2607.03414v1 Announce Type: new Abstract: This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond tradit
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Your family’s $300 stake in OpenAI Sam Altman’s proposal that
arXiv:2607.02531v1 Announce Type: cross Abstract: Water use by data centers is routinely reported as a single footprint, but water is consumed through two physically distinct pathways: at the site for
The problem with Anthropic's consciousness paper My last post got more attention than I expected, and the question I keep getting is some version of 'okay, so what is actually wrong with the paper?'.
arXiv:2607.03634v1 Announce Type: new Abstract: Artificial intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with m
arXiv:2607.04986v1 Announce Type: new Abstract: This article tackles an important phenomenon in the syntax of Yemeni Ibbi Arabic (YIA), viz., wh-agreement, a phenomenon common to several languages inc
The U.S. trade deficit rose by over 40% last month, reaching $77.6 billion. That's the largest since March 2025 and higher than the monthly average during Biden's term. Trump's tariffs have failed to
arXiv:2607.03598v1 Announce Type: cross Abstract: When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by send
arXiv:2607.03653v1 Announce Type: cross Abstract: Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybri
arXiv:2607.05125v1 Announce Type: cross Abstract: This paper presents an exploratory evaluation of how increasing levels of AI autonomy affect software development productivity, requirement adherence,
arXiv:2607.02903v1 Announce Type: cross Abstract: Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-relate
arXiv:2506.18020v3 Announce Type: replace Abstract: Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers. Such misbehaviors are commo
arXiv:2607.04442v1 Announce Type: cross Abstract: Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation pr
arXiv:2607.05089v1 Announce Type: new Abstract: Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Vi
arXiv:2607.03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodie
arXiv:2607.02593v1 Announce Type: cross Abstract: While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying sole
arXiv:2506.15020v2 Announce Type: replace-cross Abstract: We propose persistent discrete homology as a tool for topological data analysis and discuss its advantages over the existing methods. In parti
arXiv:2607.05317v1 Announce Type: cross Abstract: Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural
arXiv:2607.04593v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large
arXiv:2507.18366v2 Announce Type: replace Abstract: Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adoption of Bayesian and ensemble-based methods. Howeve
arXiv:2607.05110v1 Announce Type: cross Abstract: Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to ea
arXiv:2607.03974v1 Announce Type: new Abstract: Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has al
arXiv:2601.15235v4 Announce Type: replace-cross Abstract: Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be
arXiv:2607.03005v1 Announce Type: new Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance
arXiv:2607.04423v1 Announce Type: cross Abstract: Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains und
arXiv:2607.03210v1 Announce Type: cross Abstract: Standard machine learning training presents data as discrete endpoint pairs, omitting the structure of the space between them. This paper introduces T
arXiv:2606.18932v2 Announce Type: replace-cross Abstract: Motivated by the observational incompleteness of intermediate-to-long-period Earth-size planets, we present TransitNet, a compact attention-au
arXiv:2607.03447v1 Announce Type: cross Abstract: Knowledge graphs (KGs) that underpin Graph-based Retrieval-Augmented Generation (Graph-RAG) are increasingly built automatically by LLM-driven extract
arXiv:2607.04484v1 Announce Type: new Abstract: Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal rea
arXiv:2607.04684v1 Announce Type: new Abstract: Spatio-temporal action detection in videos requires jointly localizing actors in space and identifying action boundaries over time. A common challenge i
arXiv:2512.04632v2 Announce Type: replace Abstract: Orthogonality-based optimizers, such as Muon, have recently shown strong performance across large-scale training and community-driven efficiency cha
arXiv:2607.03176v1 Announce Type: new Abstract: Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy pol
arXiv:2607.04233v1 Announce Type: cross Abstract: Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) system
arXiv:2607.04923v1 Announce Type: new Abstract: The diagnosis of spinal diseases is often assisted by 3D imaging techniques in clinical practice. However, precise 3D spinal assessment is limited by th
arXiv:2607.04882v1 Announce Type: new Abstract: Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a
arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples o
arXiv:2607.05006v1 Announce Type: new Abstract: While various works address reflective symmetry understanding in 3D data and images, pixel-level semantic left-right prediction of in-the-wild images re