Knowledge catalogue
At Google Cloud Next, we announced the preview of graph algorithms with Spanner Graph, bringing Google Research’s state-of-the-art graph mining capabilities natively to your database. These graph inte
arXiv:2606.02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approac
arXiv:2606.00871v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used to generate structured descriptions of street-level imagery for tasks such as streetscape auditing
arXiv:2606.02215v1 Announce Type: new Abstract: Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social p
arXiv:2606.01883v1 Announce Type: cross Abstract: Open-set recognition (OSR) requires a classifier to reject inputs from unseen classes which is essential in safety-critical settings such as medical i
arXiv:2606.01695v1 Announce Type: new Abstract: Adversaries can implant latent harmful behavior by poisoning as few as 1% of fine-tuning examples. The contamination is invisible to every output-level
arXiv:2606.01494v1 Announce Type: cross Abstract: Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from bo
Google Cloud Storage (GCS) is a foundational component of the modern agentic tech stack and the preferred home for unstructured data at scale. As enterprises deploy agents in production, the critical
arXiv:2606.01863v1 Announce Type: new Abstract: Continual learning struggles to balance retaining past knowledge with absorbing new tasks. Stefan-CL elegantly resolves this stability-plasticity dilemm
arXiv:2603.27223v2 Announce Type: replace-cross Abstract: We present EuraGovExam, a multilingual and multimodal benchmark sourced from real-world civil service examinations across five representative
arXiv:2606.00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep netwo
arXiv:2606.00371v1 Announce Type: new Abstract: Muon optimizers improve neural-network training by replacing ill-conditioned momentum updates with approximately semi-orthogonal updates. This motivates
arXiv:2606.02145v1 Announce Type: new Abstract: Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-
arXiv:2606.00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transf
arXiv:2603.28825v2 Announce Type: replace-cross Abstract: Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-
Databricks introduces Cross-Engine Attribute-Based Access Control (ABAC), a feature that extends fine-grained access control policies across multiple query engines and data processing platforms within
arXiv:2509.06093v4 Announce Type: replace-cross Abstract: Materials synthesis procedures are predominantly documented as narrative text in papers, protocols, and laboratory records, placing them beyon
arXiv:2606.00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams
arXiv:2412.12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm sh
arXiv:2606.00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized s
Mathematicians from major institutions have published the Leiden Declaration on Artificial Intelligence and Mathematics, which outlines key challenges that widespread AI use poses to mathematics resea
arXiv:2606.00288v1 Announce Type: new Abstract: Large language models are undergoing a transition from model technology to system technology. As developers use Codex, Claude Code, AutoGPT, and related
arXiv:2606.00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces c
arXiv:2503.07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning. We introduce
arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as cha
OpenAI: OpenAI says it has not donated to any super PACs and does not have an employee-funded PAC, and that Greg Brockman's support for Leading the Future is personal — AI is going to be one of the mo
arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language
arXiv:2601.17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific d
arXiv:2502.20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intellige
arXiv:2602.08585v2 Announce Type: replace-cross Abstract: Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typic
arXiv:2606.01351v1 Announce Type: new Abstract: The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration to
arXiv:2606.00542v1 Announce Type: new Abstract: Shampoo-style optimizers approximate gradient covariance matrices using Kronecker-factored structures. Recent work~ite{lin2026understanding} showed that
arXiv:2606.00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental quest
arXiv:2606.01741v1 Announce Type: cross Abstract: Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservice
arXiv:2606.00962v1 Announce Type: cross Abstract: Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-age
arXiv:2606.00148v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often know the rule but pick the wrong answer: on abstract visual reasoning (AVR) tasks, a model can describe
arXiv:2606.00592v1 Announce Type: new Abstract: Effective visual communication stems from the harmony of multiple design principles, such as readability, contrast, alignment, overlap, and coherence, w
arXiv:2509.13805v4 Announce Type: replace-cross Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-train
arXiv:2606.01595v1 Announce Type: new Abstract: Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather
arXiv:2602.15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (V
arXiv:2606.01292v1 Announce Type: cross Abstract: Teacher-Student Knowledge Transfer (KT) is ubiquitous in modern machine learning, ranging from classical model compression via Knowledge Distillation
arXiv:2605.30604v1 Announce Type: cross Abstract: Regulated cybersecurity workflows lack a runtime substrate that enforces organization-level scope across retrieval, tool calls, memory, findings, repo
arXiv:2605.31468v1 Announce Type: new Abstract: Scientific research has traditionally been human-intensive, requiring researchers to coordinate literature, ideas, experiments, manuscripts, and review
arXiv:2605.31532v1 Announce Type: cross Abstract: Constitutive laws for inelastic materials must satisfy strict thermodynamic admissibility requirements, yet current data-driven approaches sacrifice i
arXiv:2602.01267v2 Announce Type: replace Abstract: Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component str
arXiv:2605.31143v1 Announce Type: cross Abstract: Rising household debt and cost-of-living pressures in the United Kingdom have intensified the role of AI-driven financial technologies in mediating cr
arXiv:2605.31438v1 Announce Type: new Abstract: Time series forecasting often requires learning nonlinear and time-delayed dependencies. A paradigmatic class of forecasting models are nonlinear vector
arXiv:2605.31257v1 Announce Type: new Abstract: Fraud detection in payment networks relies on labels generated through heterogeneous and imperfect observation processes, yet existing approaches treat
arXiv:2605.30371v1 Announce Type: cross Abstract: We address the issue of global convergence in stochastic continuous optimization. For that purpose, we formulate the Canonical Evolutionary Strategy (
arXiv:2603.08651v2 Announce Type: replace Abstract: We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning
arXiv:2605.31413v1 Announce Type: cross Abstract: We establish improved nonasymptotic bounds for Langevin Monte Carlo in the strongly log-concave setting, when the error is measured by the Wasserstein
arXiv:2605.30432v1 Announce Type: cross Abstract: Social systems consist of networks of individuals who influence one another through social interactions. Studying how processes evolve on these networ
One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tunin
OpenAI outlines its positions on AI policy priorities and explains its approach to political engagement and advocacy efforts. The document likely covers key regulatory areas OpenAI supports, such as s
arXiv:2601.11702v3 Announce Type: replace-cross Abstract: AI compliance is becoming increasingly critical as AI systems grow more powerful and pervasive. Yet the rapid expansion of AI policies creates
Today, we’re excited to announce the ability to reference a secret in AWS Secrets Manager for AgentCore Identity, so you can reference your own preconfigured secret from Secrets Manager and retain ful
arXiv:2605.30619v1 Announce Type: cross Abstract: Best-of-N sampling is widely used to construct pairwise preference data: N candidates are drawn from a base distribution, and the best is paired with
arXiv:2605.30750v1 Announce Type: new Abstract: In the era of Large Video-Language Models (LVLMs), the computational necessity of sparse frame sampling creates a fundamental ``temporal gap'', renderin
arXiv:2512.14980v4 Announce Type: replace Abstract: Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physica
arXiv:2605.30952v1 Announce Type: new Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs). On the one hand, itet{lowe2025assessing} rule out the exponential speedups claimed b