Knowledge catalogue
arXiv:2605.12532v1 Announce Type: cross Abstract: Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the seman
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from ele
AI trust infrastructure is quickly becoming a critical requirement as enterprises scale intelligent systems across their operations. The focus is shifting from simple data protection to securing, gove
arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,
arXiv:2605.12718v1 Announce Type: new Abstract: Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structu
arXiv:2509.23597v5 Announce Type: replace-cross Abstract: Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictab
arXiv:2605.13113v1 Announce Type: cross Abstract: Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting
.@Discoplomacy This kill-switch provision helps govt intervene in cases where UK datacenters are being maliciously used by a threat actor to conduct cyberattacks. It also helps in cases where the gove
arXiv:2605.13335v1 Announce Type: new Abstract: Embodied agents in household environments must plan under partial observation: they need to remember objects, track state changes, and recover when acti
This post appears to show Elon Musk in a photo with NVIDIA CEO Jensen Huang, Apple CEO Tim Cook, U.S. President Trump, Chinese President Xi Jinping, and members of a U.S. delegation, though the specif
When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party
arXiv:2605.12997v1 Announce Type: new Abstract: Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full o
arXiv:2509.19929v4 Announce Type: replace-cross Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of phy
arXiv:2605.13536v1 Announce Type: cross Abstract: High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR) -- latency and resource utilization --
arXiv:2602.04923v2 Announce Type: replace Abstract: Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general,
arXiv:2601.21892v2 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirica
arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ
arXiv:2605.12852v1 Announce Type: new Abstract: Pertussis booster vaccination produces immune responses that vary widely across individuals in both peak magnitude and long-term durability. These two p
arXiv:2605.13448v1 Announce Type: cross Abstract: Diffusion models are often trained in low-dimensional latent spaces, which are then reused for related but shifted datasets. In this work, we study wh
arXiv:2602.23089v2 Announce Type: replace Abstract: The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filte
arXiv:2511.14056v2 Announce Type: replace-cross Abstract: We study the base distribution in chart-based generative models on Riemannian manifolds. Standard methods sample in Euclidean tangent space an
arXiv:2604.01690v2 Announce Type: replace Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a
arXiv:2605.13684v1 Announce Type: new Abstract: We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensi
Secretary Lutnick is a patriot at the helm of American’s $30T economy - the largest economic system ever assembled, and deeply cares about investing in better design for Americans. @howardlutnick Secr
arXiv:2605.13426v1 Announce Type: new Abstract: Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's dec
arXiv:2602.01453v3 Announce Type: replace Abstract: We study cooperative multi-agent reinforcement learning in the setting of reward-free exploration, where multiple agents jointly explore an unknown
arXiv:2605.13252v1 Announce Type: cross Abstract: We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequent
arXiv:2605.13690v1 Announce Type: cross Abstract: Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (
arXiv:2605.12764v1 Announce Type: cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learn
arXiv:2605.12328v1 Announce Type: new Abstract: Data entry systems remain structurally vulnerable to categorical misclassifications, particularly in small and medium sized enterprises (SMEs). When nom
arXiv:2605.11759v1 Announce Type: cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeli
arXiv:2605.11287v1 Announce Type: new Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue
Clinical operations intelligence in healthcare can be effectively managed and analyzed using a lakehouse architecture, which combines the benefits of data lakes and data warehouses. This approach enab
Data quality is a foundational component of effective AI strategy, as high-quality training and operational data directly impacts model performance, reliability, and business outcomes. The article lik
arXiv:2605.11210v1 Announce Type: new Abstract: We present a framework for distributed Pose Graph Optimization (PGO) by formulating the problem as a second-order continuous-time dynamical system evolv
arXiv:2605.11567v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level act
arXiv:2605.11524v1 Announce Type: new Abstract: Data-driven identification of partial differential equations (PDEs) relies on sparse regression over a candidate library of differential operators, wher
arXiv:2605.11531v1 Announce Type: cross Abstract: Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits region
I don't understand the path forward for Mythos releases. Google & OpenAI will have equivalent models, and they are approaching AI cyber risk guardrails differently, so they will presumably just releas
arXiv:2605.11508v1 Announce Type: new Abstract: Currently, there is a gap in the field of ultra-high-definition (UHD) video dehazing due to the lack of a benchmark for evaluation. Furthermore, existin
arXiv:2509.22414v4 Announce Type: replace Abstract: Image restoration (IR) aims to recover images degraded by unknown mixtures while preserving semanticsconditions under which discriminative restorers
arXiv:2603.14094v2 Announce Type: replace-cross Abstract: We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between
arXiv:2605.11841v1 Announce Type: cross Abstract: Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theore
arXiv:2411.19093v4 Announce Type: replace Abstract: Access to drinking water and sanitation services is essential for health and well-being, yet large global disparities persist. Sustainable Developme
arXiv:2605.11142v1 Announce Type: new Abstract: Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, commu
The Cisco and AWS partnership addresses three challenges enterprises face when scaling AI agents: visibility gaps, security bottlenecks, and compliance risks. In this post, we explore how you can over
arXiv:2605.12096v1 Announce Type: new Abstract: Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource
arXiv:2605.11060v1 Announce Type: cross Abstract: Split Federated Learning (SplitFed) combines federated and split learning to preserve privacy while reducing client-side computation. However, in medi
arXiv:2605.11133v1 Announce Type: new Abstract: We introduce steerable neural ordinary differential equations on homogeneous spaces M=G/H. These models constitute a novel geometric extension of manifo
arXiv:2605.10971v1 Announce Type: cross Abstract: Discrete diffusion language models (DLMs) generate text by iteratively denoising all positions in parallel, offering an alternative to autoregressive
arXiv:2605.11538v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However
arXiv:2605.11496v1 Announce Type: cross Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently represent them, and
arXiv:2602.02281v2 Announce Type: replace-cross Abstract: Backpropagation is typically presented as a symbolic procedure: a backward pass topologically distinct from inference, with non-local error si
arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti
arXiv:2605.06042v2 Announce Type: replace Abstract: Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scal
arXiv:2605.10555v1 Announce Type: new Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD
arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread
arXiv:2605.08764v1 Announce Type: cross Abstract: Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not mere
arXiv:2605.09190v1 Announce Type: new Abstract: We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that ope
arXiv:2308.13222v3 Announce Type: replace-cross Abstract: We introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights b