Knowledge catalogue
LangSmith's LLM Gateway is a cost control and monitoring tool designed to prevent unexpected high spending on LLM API calls by providing observability and likely rate-limiting or budget-cap features.
arXiv:2605.25463v1 Announce Type: new Abstract: MedER refers to the identification of medical entities. It is crucial for extracting structured clinical information from unstructured medical text. Man
arXiv:2510.20954v3 Announce Type: replace-cross Abstract: Graphons, as limits of graph sequences, provide an operator-theoretic framework for analyzing the asymptotic behavior of graph neural operator
arXiv:2511.20236v3 Announce Type: replace Abstract: Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a
arXiv:2605.25543v1 Announce Type: new Abstract: Accurate traffic forecasting is essential for intelligent transportation systems, supporting a wide range of real-world applications. However, it remain
arXiv:2605.24203v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential for generalist robot manipulation, yet they remain limited by insufficient spatial reaso
arXiv:2511.08654v2 Announce Type: replace-cross Abstract: This paper analyses AI-generated podcasts produced by Google's NotebookLM, which generates audio podcasts with two chatty AI hosts discussing
arXiv:2605.23958v1 Announce Type: cross Abstract: M365 Copilot is used every week by millions of people across more than a million companies around the world as part of their workflows. Uniquely posit
Ethan Mollick proposes that the interrobang (‽) could serve as a distinctive marker of human-written text since AI language models rarely produce this unconventional punctuation mark. He suggests adop
arXiv:2605.24381v1 Announce Type: cross Abstract: Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve s
arXiv:2605.25135v1 Announce Type: cross Abstract: Anomaly detection in Industrial Internet of Things (IIoT) environments is essential to protect the Industrial Control Systems (ICS) and Cyber-Physical
arXiv:2310.01285v2 Announce Type: replace-cross Abstract: Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to classify regimes in time series data, and one-dimen
arXiv:2605.24631v1 Announce Type: cross Abstract: Minority sampling aims to generate low-density instances on a data manifold and is of central importance in applications such as medical diagnosis, an
arXiv:2603.24254v2 Announce Type: replace-cross Abstract: Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and
arXiv:2602.05448v4 Announce Type: replace Abstract: Selecting the top m from n items via expensive k-wise comparisons is central to settings ranging from LLM-based document reranking to crowdsourced e
arXiv:2403.04545v3 Announce Type: replace Abstract: Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free ar
arXiv:2605.25826v1 Announce Type: cross Abstract: We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a single observed trajectory of a pos
When hundreds to thousands of users are onboarded to an enterprise AI platform, business leaders and platform owners need visibility into who is using the platform, whether users are satisfied with th
arXiv:2605.25186v1 Announce Type: cross Abstract: Formalizing legal provisions promises machine-accessible law and automated legal reasoning, and recent LLMs make it tempting to generate such formaliz
arXiv:2605.24008v1 Announce Type: new Abstract: Fault detection for Deep Neural Networks (DNNs) has received increasing attention in recent years. While more advanced hybrid approaches have been propo
arXiv:2605.24696v1 Announce Type: cross Abstract: Streaming network intrusion detection systems must process flows continuously while keeping memory bounded, but most current methods leave alerting th
arXiv:2605.24340v1 Announce Type: new Abstract: Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensi
This essay by Ethan Mollick explores how individuals can maintain their humanity and agency in an increasingly AI-driven world, likely addressing practical strategies for preserving human skills, crea
arXiv:2508.03526v2 Announce Type: replace Abstract: One central goal of robotics is to enable robots to interact with the physical world. Traditional manipulation studies primarily focus on single rob
arXiv:2605.24399v1 Announce Type: new Abstract: Healthcare models are transitioning from unimodal prediction toward multimodal reasoning over heterogeneous diagnostic inputs. In computational patholog
arXiv:2410.01648v2 Announce Type: replace Abstract: The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sha
Deploy secure, encrypted mesh networking in minutes with NetBird on the Vultr Marketplace. Simplify connectivity across distributed infrastructure with WireGuard-powered networking on Vultr’s global c
arXiv:2011.10396v3 Announce Type: replace-cross Abstract: Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view c
Emmy Award-winning motion graphic designer behind 3D title sequences for Hollywood’s biggest productions, @ytok1030 bridges professional craft and AI education. See him take the stage on day two of Vi
arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful m
arXiv:2602.09620v2 Announce Type: replace Abstract: Constraint Answer Set Programming (CASP) is a hybrid paradigm that enriches Answer Set Programming (ASP) with numerical constraint processing, a cru
arXiv:2505.05371v2 Announce Type: replace-cross Abstract: Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to e
arXiv:2605.24534v1 Announce Type: new Abstract: We present a fully automated pipeline that transforms large collections of court decisions into legal commentaries for statutes - without providing any
arXiv:2602.21479v2 Announce Type: replace-cross Abstract: Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine i
arXiv:2509.23651v3 Announce Type: replace Abstract: In nature, animals often need to move/manipulate objects comparable in weight/size to their own bodies. Compared to grasping and carrying, pushing p
arXiv:2508.03104v3 Announce Type: replace-cross Abstract: Contrastive learning (CL) has become a dominant paradigm for self-supervised hypergraph learning, enabling effective training without costly l
Huge one for developers building with AI. Excited to announce @ClementDelangue (CEO @huggingface) is joining DASH 2026 for a fireside chat with @oliveur. Open source changed software, open models are
Huge thanks to @ComfyUI for publishing a deep-dive case study on how we’re building enterprise-grade, scalable AI pipelines. Arron Award Winning work for AI workflow. Read the full breakdown: https://
arXiv:2605.24180v1 Announce Type: cross Abstract: Collaboration is the defining mode of modern science, yet its core mechanism -- feedback -- remains hard to observe, difficult to scale, and unequally
arXiv:2507.02215v2 Announce Type: replace-cross Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with he
arXiv:2605.24528v1 Announce Type: new Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of
I wrote a new post on what we need to keep human and what to hand over to AI, with forays into experiments in education, consulting, and the the latest controversy over literary prizes. https://www.on
In fact, I would be sympathetic to the conclusion that we probably need even more fact checkers, and much more of their time can be freed up to do complex and interesting work by using AI for first-pa
arXiv:2605.23908v1 Announce Type: new Abstract: We are in the midst of large-scale industrial and academic efforts to automate the processes of scientific, technological and creative production throug
Infinite context windows seem to present a very large problem to using AI. Today's models already leak too much old information into current responses, a distraction that is part of why they are cogni
arXiv:2508.21620v2 Announce Type: replace Abstract: Decision theories offer principled methods for making choices under various types of uncertainty. Algorithms that implement these theories have been
arXiv:2605.25750v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) are a powerful framework for learning representations of graph-structured domains. However, weights in MPNNs act
arXiv:2605.24647v1 Announce Type: new Abstract: Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interactio
arXiv:2605.22093v2 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modellin
arXiv:2605.26040v1 Announce Type: new Abstract: Graph fraud detection has long depended on Graph Neural Networks (GNNs) to propagate and aggregate information across relational data. A critical obstac
arXiv:2605.24515v1 Announce Type: new Abstract: With climate change and increasing human pressure on natural landscapes, inland water resources are becoming progressively scarcer, more vulnerable, and
arXiv:2602.02839v3 Announce Type: replace Abstract: Enabling robots to perform novel manipulation tasks from natural language instructions remains a fundamental challenge in robotics, despite signific
arXiv:2605.25581v1 Announce Type: new Abstract: Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, pro
arXiv:2506.06454v2 Announce Type: cross Abstract: Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future predict
arXiv:2605.24077v1 Announce Type: cross Abstract: Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-spe
arXiv:2605.24813v1 Announce Type: new Abstract: Sampling-based model predictive control methods, such as Model Predictive Path Integral (MPPI), offer derivative-free optimization and robustness in com
arXiv:2605.24111v1 Announce Type: cross Abstract: Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent ge
arXiv:2605.24961v1 Announce Type: new Abstract: Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existin
arXiv:2510.05699v4 Announce Type: replace-cross Abstract: Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these a
arXiv:2502.01397v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been proposed as a tool for learning sparse matrix preconditioners, which are key components in accelerating