Knowledge catalogue
arXiv:2606.27515v1 Announce Type: new Abstract: Accurate prediction of bottom-hole pressure (BHP) and CO2 plume migration is essential for safe geological carbon storage, yet practical simulations oft
arXiv:2606.28050v1 Announce Type: cross Abstract: LLM-as-a-Judge and self-evaluation pipelines implicitly assume that evaluation is easier than generation. We test this in a controlled in-context QA s
arXiv:2511.02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10% of the global population and often progresses to end-stage renal failure. Accurate prognosis predict
arXiv:2606.27696v1 Announce Type: cross Abstract: In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For sc
arXiv:2606.27584v1 Announce Type: cross Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints. While recent methods leverage Gaussian Splatt
arXiv:2411.07175v3 Announce Type: replace Abstract: As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is
arXiv:2606.27935v1 Announce Type: new Abstract: Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models cr
arXiv:2606.27629v1 Announce Type: cross Abstract: Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold
arXiv:2606.28104v1 Announce Type: new Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action
arXiv:2601.14302v2 Announce Type: replace-cross Abstract: Image transmission and processing systems in resource-critical applications face significant challenges from adversarial perturbations that co
arXiv:2606.27635v1 Announce Type: new Abstract: This paper documents the implementation and evaluation of a self-supervised denoising framework on Inertial Confinement Fusion (ICF) images corrupted by
arXiv:2501.07400v2 Announce Type: replace-cross Abstract: We derive explicit equations governing the cumulative biases and weights in Deep Learning with ReLU activation function, based on gradient des
arXiv:2606.28048v1 Announce Type: cross Abstract: Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. Whil
arXiv:2606.27767v1 Announce Type: new Abstract: Optimizing functionals over the space of probability measures is now ubiquitous in machine learning. A widely used approach is to perform the optimizati
arXiv:2606.28136v1 Announce Type: cross Abstract: Extreme adaptive optics (AO) is necessary for high contrast astronomy at scales of the habitable zone of nearby systems. We seek to evaluate wavefront
arXiv:2606.28092v1 Announce Type: new Abstract: Attributing a generated image to its source diffusion model is a fundamental challenge in provenance verification and intellectual property protection.
arXiv:2606.27455v1 Announce Type: cross Abstract: We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observation
arXiv:2606.28228v1 Announce Type: new Abstract: Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability i
arXiv:2606.27596v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination. Deviating from the prevailing att
arXiv:2606.27579v1 Announce Type: cross Abstract: Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis. Key challe
arXiv:2606.27755v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs
arXiv:2504.11299v2 Announce Type: replace-cross Abstract: We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multi-dimensional setting, and make new argument
arXiv:2606.27320v1 Announce Type: cross Abstract: Neural audio autoencoders have become a core component of compression, feature extraction, and generation. However, while existing systems support var
arXiv:2606.28026v1 Announce Type: new Abstract: High-fidelity and expressive controllable human animation is essential for content creation and digital avatar applications. However, existing methods f
arXiv:2606.27550v1 Announce Type: new Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto ap
arXiv:2603.25168v2 Announce Type: replace Abstract: Previous works based on Segment Anything Model (SAM) have achieved promising performance in unified scene text detection and layout analysis. Howeve
Finally, a people-search tool that actually works. Most people-search tools sell you a frozen list. @CLODOAI searches the live web, reads the signals, and tells you why this specific person, right now
arXiv:2606.27751v1 Announce Type: cross Abstract: This report investigates the extension of pretrained General-Purpose Audio Tagging (GP-AT) models toward spatially grounded Sound Event Localization a
arXiv:2308.05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functi
arXiv:2606.27863v1 Announce Type: cross Abstract: Demand forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores
arXiv:2603.18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-
arXiv:2606.27831v1 Announce Type: cross Abstract: This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framew
arXiv:2606.27923v1 Announce Type: cross Abstract: We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior
arXiv:2606.28242v1 Announce Type: cross Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on s
arXiv:2606.27627v1 Announce Type: cross Abstract: Discrete audio representations have become increasingly popular for building multimodal text-audio systems and integrating audio capabilities into Lar
arXiv:2604.00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidl
arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models
arXiv:2606.27759v1 Announce Type: new Abstract: Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces sever
arXiv:2606.27808v1 Announce Type: new Abstract: A minute lexical variation can reverse the procedural meaning of an instruction even when the rest of the sentence remains unchanged. In automotive main
arXiv:2606.28196v1 Announce Type: new Abstract: Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects be
arXiv:2601.12066v4 Announce Type: replace Abstract: Existing video object removal methods predominantly rely on diffusion models following a noise-to-data paradigm, where generation starts from uninfo
arXiv:2606.27721v1 Announce Type: new Abstract: Compositional generalization, the ability to solve complex problems by combining solutions to simpler sub-problems, is a fundamental capability of both
arXiv:2510.25731v2 Announce Type: replace-cross Abstract: Initial-boundary value problems (IBVPs) provide the essential framework for modelling a wide range of phenomena in physics and engineering. We
arXiv:2606.28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby spee
arXiv:2606.27617v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approxim
arXiv:2603.05121v2 Announce Type: replace-cross Abstract: Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters
arXiv:2606.28116v1 Announce Type: new Abstract: Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they o
arXiv:2606.27652v1 Announce Type: new Abstract: We find that explicit reasoning does not necessarily translate into better multimodal emotion recognition (MER) accuracy, even though it makes predictio
arXiv:2606.28039v1 Announce Type: cross Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely avail
arXiv:2606.27779v1 Announce Type: new Abstract: Generating lifelike facial animation for dyadic conversations requires reconciling high-level cognitive intent with precise low-level motor reflexes, ye
arXiv:2606.27687v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests. However, L
arXiv:2606.28142v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) methods commonly update the affine parameters of normalization layers to adapt deployed models under distribution shifts. How
arXiv:2606.27671v1 Announce Type: new Abstract: Predicting electromagnetic field (EMF) strength in urban environments is essential for cellular network planning but computationally expensive with phys
Musk is right that many problems can't be solved just by 'throwing money' at them. But US aid to fight AIDS, child hunger & other global plagues has one of the highest ROIs in government. The data is
arXiv:2410.02103v4 Announce Type: replace Abstract: Recent works in volume rendering, extit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with
arXiv:2606.27598v1 Announce Type: cross Abstract: Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail. We hypo
arXiv:2606.27791v1 Announce Type: cross Abstract: Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but t
arXiv:2606.28204v1 Announce Type: cross Abstract: Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-f
arXiv:2606.27605v1 Announce Type: cross Abstract: We study the stability of a classical family of metrics defined over functions' Gaussian scale-space representations, focusing on the comparison of im
One study found Musk’s closing of the U.S. Agency for International Development could cause the deaths of millions of children. https://www.forbes.com/sites/tylerroush/2026/06/29/a-testy-musk-attacks-