Knowledge catalogue
arXiv:2512.16545v3 Announce Type: replace-cross Abstract: Cu NPs have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with
arXiv:2610.09126v1 Announce Type: new Abstract: Accurate thermal modeling is essential in metal additive manufacturing (AM) for understanding the process-structure-property chain. Physics-informed neu
arXiv:2610.09635v1 Announce Type: cross Abstract: Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains,
arXiv:2610.09617v1 Announce Type: cross Abstract: We introduce a novel second-order optimization framework for minimizing so-called variable projection functionals. We demonstrate that the proposed fr
Editor’s note: This article is adapted from Thomas Kurian’s keynote address at Gemini at Work 2026. It includes a number of announcements: Gemini, a single, universal agent for work that answers your
arXiv:2610.08969v1 Announce Type: new Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency
arXiv:2407.07111v2 Announce Type: replace-cross Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making 'what you want is what you see
arXiv:2610.07298v1 Announce Type: cross Abstract: Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources.
arXiv:2610.08287v1 Announce Type: new Abstract: Understanding the structural relationships among attributes in tabular data is fundamental to machine learning and pattern recognition. While functional
arXiv:2610.04342v1 Announce Type: cross Abstract: Inter-robot communication is a key enabling technology in cooperative robotics applications. While wireless communication is ubiquitous, simple, and e
arXiv:2607.14539v2 Announce Type: replace Abstract: Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encounter
DOCA GPUNetIO delivers a unified GPU‑directed networking (GDA‑KI) foundation that lets CUDA kernels drive Ethernet, RDMA, Verbs and DMA operations without the CPU entering the application path. It shi
arXiv:2610.04640v1 Announce Type: cross Abstract: Hypergraphs record multi-way interactions among entities. Extracting information from the combinatorial structure underlying observed multi-way intera
arXiv:2510.16587v2 Announce Type: replace-cross Abstract: Understanding the continuous evolution of populations from discrete temporal snapshots is a critical research challenge, particularly in field
arXiv:2610.05695v1 Announce Type: new Abstract: Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was p
Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrie
arXiv:2609.35013v2 Announce Type: replace Abstract: The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consu
arXiv:2610.03193v1 Announce Type: new Abstract: The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential appl
arXiv:2610.02320v1 Announce Type: cross Abstract: Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually s
arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and
arXiv:2610.02610v1 Announce Type: new Abstract: The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations
arXiv:2610.02386v1 Announce Type: cross Abstract: We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based c
arXiv:2610.01893v1 Announce Type: cross Abstract: By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, he
arXiv:2610.01286v1 Announce Type: new Abstract: Recent depth foundation models like Depth Anything 3 (DA3) achieve remarkable multi-view depth estimation but assume static 3D scenes, limiting their ap
arXiv:2610.00209v1 Announce Type: new Abstract: Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may co
arXiv:2603.11546v2 Announce Type: replace Abstract: Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multi
arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes
arXiv:2610.01759v1 Announce Type: new Abstract: Generating and predicting spatiotemporal physical fields from scarce measurements is challenging, as observations are insufficient to characterize a dis
arXiv:2609.39564v1 Announce Type: new Abstract: Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs thro
arXiv:2609.38864v1 Announce Type: new Abstract: Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as i
arXiv:2609.38506v1 Announce Type: new Abstract: Tree-like branching structures are common in nature, from botanical trees to neurons, blood vessels and respiratory trees. Their branching shape often r
arXiv:2609.39843v1 Announce Type: cross Abstract: Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based
arXiv:2609.38196v1 Announce Type: cross Abstract: Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence
arXiv:2603.08459v2 Announce Type: replace Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trust
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In
arXiv:2609.38402v1 Announce Type: cross Abstract: We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across mul
arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects maliciou
arXiv:2609.36771v1 Announce Type: cross Abstract: Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a syst
arXiv:2609.37568v1 Announce Type: new Abstract: Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual, a
arXiv:2501.18157v2 Announce Type: replace-cross Abstract: Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions fre
arXiv:2609.38089v1 Announce Type: cross Abstract: Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological
arXiv:2602.03915v2 Announce Type: replace-cross Abstract: Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be
arXiv:2609.37809v1 Announce Type: cross Abstract: Predicting canopy height from medium-resolution satellite imagery is a common and scalable approach for assessing the condition of the world's forests
arXiv:2501.03598v2 Announce Type: replace-cross Abstract: Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth o
arXiv:2603.12057v3 Announce Type: replace Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various rea
arXiv:2609.33758v1 Announce Type: new Abstract: Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than
arXiv:2609.33167v1 Announce Type: new Abstract: We present FloodDiffusion 2 (FD2), an efficient and controllable framework that builds upon FloodDiffusion (FD1), a state-of-the-art streaming motion ge
arXiv:2511.02468v2 Announce Type: replace-cross Abstract: Mobile eye-tracking is crucial for capturing human visual attention in real-world and XR settings, supporting research and human-computer inte
arXiv:2609.33174v1 Announce Type: new Abstract: Advancing bipedal robots to navigate diverse terrains remains a significant challenge in robotics. Traditional locomotion controllers excel on specific
arXiv:2505.09274v2 Announce Type: replace Abstract: Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various ta
arXiv:2609.33685v1 Announce Type: new Abstract: Artificial Intelligence (AI) models for temporal multimodal data have potential in healthcare and agriculture, but their opacity can limit trust and ado
arXiv:2609.34156v1 Announce Type: new Abstract: Prediction-powered inference (PPI) and its power-tuned extension (PPI++) improve confidence intervals by combining a small gold-standard labeled sample
arXiv:2609.31697v1 Announce Type: new Abstract: Low-light conditions can significantly degrade the ability of vision-language models (VLMs) to accurately describe human actions in videos. In this work
arXiv:2411.04707v4 Announce Type: replace Abstract: Deep neural networks achieve strong performance on complex tasks but are often regarded as 'black boxes,' which limits their adoption in domains whe
arXiv:2510.05191v3 Announce Type: replace-cross Abstract: Conditional generation and disentangled representation learning are central to controlled generation across audio, vision, and multimodal doma
Automated lint: 17 errors, 59669 warnings, 3 info
arXiv:2609.28915v1 Announce Type: cross Abstract: LLM agents are deployed into infrastructure that grants them broad host authority, yet existing agent-security benchmarks and defenses operate almost
arXiv:2505.15465v2 Announce Type: replace Abstract: This paper introduces Synthetic Enclosed Echoes (SEE), a novel dataset designed to enhance robot perception and 3D reconstruction capabilities in un
arXiv:2609.27903v1 Announce Type: new Abstract: Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviati
arXiv:2609.27330v1 Announce Type: new Abstract: We develop a sample-based framework for constructing information-driven hierarchical multi-resolution representations of probabilistic occupancy grids.