Knowledge catalogue
Mind Robotics Inc., an industrial robotics startup founded by Rivian Automotive Inc. Chief Executive RJ Scaringe, said today it has raised 400 million in new funding to put more of its artificial inte
arXiv:2605.11840v1 Announce Type: new Abstract: Radar-camera depth estimation must turn an ultra-sparse, all-weather, metric radar signal into a dense per-pixel depth map. Existing methods -- concaten
arXiv:2605.12163v1 Announce Type: new Abstract: In language reasoning, longer chains of thought consistently yield better performance, which naturally suggests that visual latent reasoning may likewis
arXiv:2605.12096v1 Announce Type: new Abstract: Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource
Carmen Arroyo / Bloomberg: Sources: Apollo Global, Morgan Stanley, and others are testing Grok internally as part of xAI's finance sector push, but financiers are rarely using it for work — Elon Musk'
arXiv:2605.11060v1 Announce Type: cross Abstract: Split Federated Learning (SplitFed) combines federated and split learning to preserve privacy while reducing client-side computation. However, in medi
Stop turning prompting into magic spells (and yes, this includes random slash commands with obscure outcomes). Let this one area of working with AI not be weird. Just ask for stuff, in well-specified
arXiv:2602.22586v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy.
arXiv:2603.23878v2 Announce Type: replace Abstract: The parameterized CROWN analysis, a.k.a., alpha-CROWN has emerged as a practically successful abstract interpretation method for neural network veri
arXiv:2605.12077v1 Announce Type: new Abstract: Jigsaw puzzle solving has been an increasingly popular task in the computer vision research community. Recent works have utilized cutting-edge architect
arXiv:2605.12462v1 Announce Type: cross Abstract: Extreme weather and volatile wholesale electricity markets expose residential consumers to catastrophic financial risks, yet demand response at the di
arXiv:2602.13004v2 Announce Type: replace Abstract: Granger causality recovers directed interactions from time-series data, but in many distributed systems, the data are vertically partitioned across
arXiv:2605.11993v1 Announce Type: new Abstract: Movie subtitle translation is inherently multimodal, yet text-only systems often miss visual cues needed to convey emotion, action, and social nuance, e
arXiv:2605.11856v1 Announce Type: cross Abstract: Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on
arXiv:2605.11865v1 Announce Type: cross Abstract: Standard Bradley--Terry (BT) reward models are limited when human preferences are pluralistic. Although soft preference labels preserve disagreement i
arXiv:2605.12255v1 Announce Type: cross Abstract: When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other pa
arXiv:2605.12162v1 Announce Type: new Abstract: Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation. Existing meth
arXiv:2605.08207v1 Announce Type: new Abstract: Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. T
arXiv:2605.10256v1 Announce Type: cross Abstract: Most recent advances in audio dereverberation focus almost exclusively on speech, leaving percussive and drum signals largely unexplored despite their
arXiv:2605.10181v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection is essential for building reliable AI systems, as models that produce outputs for invalid inputs cannot be trusted
arXiv:2510.19407v2 Announce Type: replace-cross Abstract: A sensor has the ability to probe its surroundings. However, uncertainties in its exact location can significantly compromise its sensing perf
arXiv:2605.09699v1 Announce Type: cross Abstract: Modern computer vision systems increasingly encounter performance limitations in data-scarce domains, where collecting large-scale, high-quality label
arXiv:2605.10651v1 Announce Type: cross Abstract: Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it
arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti
arXiv:2605.09094v1 Announce Type: new Abstract: In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing w
arXiv:2605.09678v1 Announce Type: new Abstract: While extremely powerful and versatile at various tasks, the thinking capabilities of large language models (LLMs) are often put under scrutiny as they
arXiv:2605.08429v1 Announce Type: cross Abstract: Post-deployment monitoring of healthcare AI requires statistically valid, label-efficient methods, but gold-standard labels from clinician chart revie
arXiv:2512.13919v2 Announce Type: replace Abstract: This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models
arXiv:2605.09115v1 Announce Type: cross Abstract: Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although
As enterprises push AI beyond the pilot stage, the cost and complexity of running inference at scale are forcing a fundamental rethink of how infrastructure is designed, governed and sourced, putting
arXiv:2603.25561v2 Announce Type: replace Abstract: Saccharomyces cerevisiae is increasingly recognised as a key source for single-cell protein (SCP) production, a rising solution to global protein-su
arXiv:2603.16964v2 Announce Type: replace Abstract: Approval of ADS depends on evaluating its behavior within representative real-world traffic scenarios. A common way to obtain such scenarios is to e
arXiv:2602.04189v2 Announce Type: replace Abstract: Uncertainty evaluation is critical in scientific and engineering inverse problems. However, existing benchmarks on Diffusion Inverse Solvers (DIS) p
arXiv:2605.09579v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of global mortality, yet scalable cardiac monitoring is hindered by the gap between diagnostic-rich E
arXiv:2602.07144v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the paramete
arXiv:2605.08938v1 Announce Type: new Abstract: Fourier Neural Operators (FNOs) can greatly accelerate PDE simulation, but they are often used without formal guarantees that they preserve basic physic
arXiv:2605.08173v1 Announce Type: new Abstract: The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training dataset
arXiv:2507.03310v2 Announce Type: replace-cross Abstract: This paper studies causal discovery in irregularly sampled time series-a key challenge in risk-sensitive domains like finance, healthcare, and
arXiv:2603.02678v3 Announce Type: replace Abstract: This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and r
arXiv:2601.22427v2 Announce Type: replace-cross Abstract: Temporal link prediction is crucial for rapidly growing social networks. Existing methods often overlook the underlying causal mechanisms that
arXiv:2511.07055v3 Announce Type: replace Abstract: High-stakes decisions informed by decision support systems require explicit evidence. While prior work focuses on short sufficient evidence, regulat
arXiv:2605.10847v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f
arXiv:2602.07209v2 Announce Type: replace Abstract: Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g
arXiv:2605.10887v1 Announce Type: new Abstract: Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far
arXiv:2605.10894v1 Announce Type: new Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardwa
arXiv:2605.10688v1 Announce Type: new Abstract: Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to
arXiv:2605.10166v1 Announce Type: new Abstract: Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or e
arXiv:2509.22531v2 Announce Type: replace-cross Abstract: In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-do
arXiv:2510.07136v2 Announce Type: replace-cross Abstract: We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flip
arXiv:2605.09719v1 Announce Type: cross Abstract: Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs.
arXiv:2605.09347v1 Announce Type: new Abstract: Discrete variables are common in many applications, such as probabilistic reasoning, planning and explainable AI. When symbolic reasoning techniques are
arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap
arXiv:2510.19414v2 Announce Type: replace-cross Abstract: The growing prevalence of speech deepfakes has raised serious concerns, particularly in real-world scenarios such as telephone fraud and ident
arXiv:2605.08273v1 Announce Type: cross Abstract: Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administrat
arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment
arXiv:2507.06658v2 Announce Type: replace-cross Abstract: Theories of democratic stability, populism, and party-system crisis often point to a form of polarization that comparative research rarely mea
arXiv:2605.09509v1 Announce Type: cross Abstract: The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as re
arXiv:2604.16838v2 Announce Type: replace-cross Abstract: We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attest
arXiv:2507.18847v3 Announce Type: replace-cross Abstract: We propose a new volumetric grasp model that is equivariant to rotations around the vertical axis, leading to a significant improvement in sam
arXiv:2605.09982v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real