Knowledge catalogue
At Google Cloud Next ‘26, we unveiled the blueprint for the Agentic Enterprise, sharing our eighth-generation TPUs, Gemini Enterprise Agent Platform, a fully reimagined Agentic Data Cloud, Workspace I
arXiv:2601.05653v2 Announce Type: replace Abstract: Existing traffic simulation frameworks for autonomous vehicles typically rely on imitation learning or game-theoretic approaches that solve for Nash
arXiv:2605.17721v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions,
arXiv:2501.12709v2 Announce Type: replace-cross Abstract: Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum fe
arXiv:2603.11689v2 Announce Type: replace Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are of
arXiv:2605.18036v1 Announce Type: cross Abstract: Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evalua
arXiv:2406.15797v2 Announce Type: replace-cross Abstract: Employing graph neural networks (GNNs) for graph clustering has shown promising results in deep graph clustering. However, existing methods di
arXiv:2605.17165v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPA) are a promising framework for self-supervised video representation learning, yet the behavior of auxilia
arXiv:2605.16350v1 Announce Type: cross Abstract: We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules,
arXiv:2509.06984v3 Announce Type: replace-cross Abstract: Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their lar
arXiv:2605.17246v1 Announce Type: cross Abstract: We introduce fidelity probes: natural-language questions generated from a reference artifact with code-derived ground-truth answers, answered from a c
arXiv:2505.20218v2 Announce Type: replace Abstract: Accurate and safe medication recommendations are critical for effective clinical decision-making, especially in multimorbidity cases. However, exist
arXiv:2605.17693v1 Announce Type: cross Abstract: Structure-based drug design has been accelerated by pocket-aware 3D generative models, yet most methods primarily fit the training distribution and ma
arXiv:2605.17558v1 Announce Type: cross Abstract: Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that
arXiv:2605.16469v1 Announce Type: cross Abstract: Rare diseases dominate the diagnostic challenge in medical imaging yet are severely underrepresented in clinical datasets, causing classifiers to fail
arXiv:2605.17590v1 Announce Type: new Abstract: We formulate machine unlearning for online L-BFGS as a counterfactual state-alignment problem. Given an actual event stream and a deletion-edited counte
arXiv:2605.18204v1 Announce Type: cross Abstract: Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete dif
arXiv:2508.06038v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of vision tokens introduc
arXiv:2605.16366v1 Announce Type: cross Abstract: Video MLLMs face a persistent tension between spatial fidelity and temporal coverage: preserving fine-grained visual details requires many spatial tok
arXiv:2605.16927v1 Announce Type: new Abstract: Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clini
arXiv:2605.18271v1 Announce Type: cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and
arXiv:2605.16928v1 Announce Type: cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely ei
arXiv:2509.19102v2 Announce Type: replace-cross Abstract: General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the trainin
arXiv:2605.17969v1 Announce Type: new Abstract: Despite rapid advances in text-to-image generation, faithfully realizing user intent remains challenging, often requiring manual multi-turn trial and er
arXiv:2507.21035v3 Announce Type: replace Abstract: Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains formidable due t
arXiv:2505.20914v2 Announce Type: replace Abstract: General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simul
arXiv:2602.08206v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation has become an important direction in remote sensing, as it enables recognition beyond predefined land-cover ca
arXiv:2605.16713v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behi
arXiv:2602.20200v2 Announce Type: replace-cross Abstract: Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a V
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On T
Google’s new Gemini 3.5 Flash is the clear leader on the Intelligence vs Speed Pareto frontier and makes large gains on GDPval-AA (real-world agentic tasks), but is 5x the cost of Gemini 3 Flash @Goog
arXiv:2506.14009v2 Announce Type: replace Abstract: Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Y
arXiv:2602.07884v2 Announce Type: replace-cross Abstract: Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models offer interpretabi
arXiv:2508.02383v2 Announce Type: replace Abstract: Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spec
arXiv:2605.18387v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) and Graph Transformers (GTs) are now a fundamental paradigm for graph learning, combining the representation-learning cap
arXiv:2605.16668v1 Announce Type: cross Abstract: We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces
arXiv:2605.16873v1 Announce Type: new Abstract: Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at
arXiv:2602.16346v3 Announce Type: replace Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to ca
arXiv:2605.17570v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has been a key driver of recent progress in reinforcement learning with verifiable rewards (RLVR) for large
arXiv:2605.17792v1 Announce Type: new Abstract: Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir oper
arXiv:2605.16527v1 Announce Type: cross Abstract: Hypergraphs model higher-order relations that drive real-world decisions, from drug prescriptions to recommendations. A central structural signal in s
arXiv:2603.00607v2 Announce Type: replace-cross Abstract: Multi-subject image generation requires seamlessly harmonizing multiple reference identities within a coherent scene. However, existing method
arXiv:2605.18500v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly leveraged tool invocation to enhance their reasoning capabilities. However, existing approaches typically
arXiv:2509.18150v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated outstanding performance across a variety of domains. However, training MLLMs is oft
At I/O 2026, Google announced that AI is transitioning from something users actively open to a background service that completes tasks automatically. The company introduced Gemini Spark, a new agentic
arXiv:2605.16708v1 Announce Type: new Abstract: Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent co
arXiv:2605.18657v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) have demonstrated notable success in general-purpose forecasting tasks; however, their adaptation to specialized
arXiv:2605.16376v1 Announce Type: cross Abstract: Kelvin is a lightweight learned pre-encoder that sits in front of an unmodified libx264 encoder. It applies content-adaptive pixel adjustments, bounde
arXiv:2605.17856v1 Announce Type: new Abstract: Process-based simulation models encode decades of scientific understanding across the Earth sciences, yet the communities most exposed to climate risk a
arXiv:2605.16439v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have emerged as a critical and fast-growing extension of Large Language Models (LLMs) that enable multimodal reasoning t
arXiv:2605.18071v1 Announce Type: new Abstract: Supporting long-context LLMs is challenging due to the substantial memory demands of the key-value (KV) cache. Existing offloading systems store the ful
arXiv:2605.16352v1 Announce Type: cross Abstract: Repository-level coding agents must first localize the files and symbols relevant to a task; failures at this stage can cascade across downstream obje
arXiv:2605.16899v1 Announce Type: new Abstract: A fundamental challenge in embodied AI is verifying if agents build internal models of spatial structure or merely learn to mimic task-specific expert t
arXiv:2605.18597v1 Announce Type: new Abstract: Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inf
arXiv:2601.01123v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A
arXiv:2601.22297v2 Announce Type: replace Abstract: The reasoning abilities of large language models (LLMs) have been substantially improved by reinforcement learning with verifiable rewards (RLVR). A
arXiv:2605.17316v1 Announce Type: cross Abstract: Sensor networks increasingly govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standar
arXiv:2605.17238v1 Announce Type: new Abstract: We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and i
arXiv:2605.17058v1 Announce Type: new Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Sto
arXiv:2505.19590v5 Announce Type: replace-cross Abstract: Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited