Knowledge catalogue
arXiv:2607.06739v1 Announce Type: new Abstract: Event-based cameras are gaining popularity as the sensor of choice for mobile robotics, due to their high performance in dynamic environments. However,
arXiv:2607.06696v1 Announce Type: cross Abstract: Weighted empirical measures on compact manifolds arise in importance sampling, particle approximations, posterior summaries, quadrature, and represent
arXiv:2607.07401v1 Announce Type: cross Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acq
arXiv:2607.07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidenc
arXiv:2607.07678v1 Announce Type: new Abstract: Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-
arXiv:2410.07719v4 Announce Type: replace Abstract: Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing emp
arXiv:2607.07114v1 Announce Type: cross Abstract: Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adv
arXiv:2607.07233v1 Announce Type: new Abstract: Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic
arXiv:2607.06972v1 Announce Type: new Abstract: Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing method
arXiv:2607.06976v1 Announce Type: cross Abstract: In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes
arXiv:2607.07330v1 Announce Type: cross Abstract: Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored.
arXiv:2607.07430v1 Announce Type: new Abstract: Humanoid robots can extend human presence to remote, constrained, or hazardous environments, but existing teleoperation interfaces often require physica
arXiv:2607.07034v1 Announce Type: cross Abstract: We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a manifold as the solution to a linear PDE whose source te
arXiv:2607.07085v1 Announce Type: cross Abstract: The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused. F
Yann LeCun argues that the primary risk posed by AI lies in power concentration among a small number of proprietary AI assistant providers, suggesting that decentralization or open-source alternatives
arXiv:2607.06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy withou
arXiv:2607.07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frame
arXiv:2607.07518v1 Announce Type: new Abstract: Deformable shape representations have proven to be robust complements to texture features in cardiac image classification, offering geometric priors tha
arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain
arXiv:2607.06719v1 Announce Type: cross Abstract: This paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed mo
arXiv:2606.16673v2 Announce Type: replace Abstract: Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising
arXiv:2510.25370v2 Announce Type: replace Abstract: Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Commu
arXiv:2607.07573v1 Announce Type: new Abstract: Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual ste
arXiv:2607.07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot
arXiv:2501.02436v5 Announce Type: replace Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we pro
arXiv:2607.07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-co
arXiv:2607.07168v1 Announce Type: new Abstract: Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance
arXiv:2603.23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known envi
arXiv:2607.07375v1 Announce Type: cross Abstract: Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, inp
Open source powers everything in America. We must fight any and all attempts to restrict it in the age of AI. We must never surrender to the short-sighted safetyists and hawks. Open source is the enti
arXiv:2607.06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our metho
arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However,
arXiv:2503.15770v3 Announce Type: replace-cross Abstract: Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to a
arXiv:2607.06999v1 Announce Type: cross Abstract: This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowled
arXiv:2607.06585v1 Announce Type: new Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in econ
arXiv:2607.07374v1 Announce Type: new Abstract: Dynamic environments remain a fundamental challenge for visual SLAM, where unreliable observations from moving objects and rapid motion degrade state es
arXiv:2603.15245v2 Announce Type: replace Abstract: Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) ge
arXiv:2607.07146v1 Announce Type: cross Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time
arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or
arXiv:2607.07170v1 Announce Type: new Abstract: Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D grap
arXiv:2607.07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A ma
arXiv:2607.06829v1 Announce Type: new Abstract: Rail track extraction is essential for effective railway asset management and maintenance, especially in automated inspection and mapping workflows. Thi
arXiv:2607.07646v1 Announce Type: new Abstract: Does RL post-training merely amplify primitive skills already latent in a base model, or can it compose primitive skills into new higher-level strategie
arXiv:2603.00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quant
arXiv:2607.07492v1 Announce Type: new Abstract: Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, an
arXiv:2607.06909v1 Announce Type: new Abstract: Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (
arXiv:2512.17648v2 Announce Type: replace Abstract: Streaming Speech-to-Text Translation (StreamST) requires producing translations concurrently with incoming speech under strict latency constraints,
arXiv:2607.07542v1 Announce Type: cross Abstract: Powered prosthetic hands are frequently abandoned, largely due to the limited functionality of current devices that rely on surface electromyography (
arXiv:2607.06620v1 Announce Type: cross Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry.
arXiv:2607.07386v1 Announce Type: new Abstract: Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention mod
arXiv:2607.06949v1 Announce Type: new Abstract: Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic h
arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base
arXiv:2607.06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and
arXiv:2501.18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized
arXiv:2502.00037v4 Announce Type: replace-cross Abstract: We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, wi
arXiv:2607.06841v1 Announce Type: cross Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approac
arXiv:2607.06642v1 Announce Type: new Abstract: Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common h
Meta has made significant progress on its superintelligence research agenda, which aims to develop advanced AI systems with broad capabilities. The update covers Meta's infrastructure investments, res
arXiv:2607.07423v1 Announce Type: new Abstract: We prove that, in the realizable PAC setting, the sample complexity of exact-trace learning for full autoregressive Chain-of-Thought traces is upper bou
arXiv:2607.06640v1 Announce Type: cross Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the mo