Every day, millions of developers write code, create database architectures, and optimize engines. We are used to thinking that programming — is an artificial construct. It was invented by humans for solving utilitarian tasks. But if one steps away from the monitor and looks at how tangible existence is built, a terrifying similarity can be noticed. The Universe, perhaps, is not just “similar” to a computer. It fundamentally utilizes the same engineering principle on which the entire IT […]

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World models aim to enable agents to perceive states, predict future outcomes, and reason for decision-making by simulating real-world environments, and are widely regarded as a crucial pathway toward artificial general intelligence (AGI). Video, as one of the most accessible and intuitively representative media of dynamic environments, naturally contains rich implicit representations of the physical world. Consequently, learning world models from videos has become a prominent research direction. However, a significant gap remains between video data and the […]

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Production query plans without production data Radim Marek describes the new pg_restore_relation_stats() and pg_restore_attribute_stats() functions that were introduced in PostgreSQL 18 in September 2025. The PostgreSQL query planner makes use of internal statistics to help it decide how to best execute a query. These statistics often differ between production data and development environments, which means the query plans used in production may not be replicable in development. PostgreSQL’s new features now let you copy those statistics down to […]

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arXiv:2504.13336v2 Announce Type: replace Abstract: Flow Matching has recently gained attention in generative modeling as a simple and flexible alternative to diffusion models. While existing statistical guarantees adapt tools from the analysis of diffusion models, we take a different perspective by connecting Flow Matching to kernel density estimation. We first verify that the kernel density estimator matches the optimal rate of convergence in Wasserstein distance up to logarithmic factors, improving existing bounds for the Gaussian kernel. Based on […]

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arXiv:2503.16941v3 Announce Type: replace Abstract: Personalized services are central to today’s digital economy, and their sequential decisions are often modeled as contextual bandits. Modern applications pose two main challenges: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate relationships. We propose SPARKLE, a novel contextual bandit algorithm based on a sparse additive reward model that addresses both challenges through (i) a doubly penalized estimator for nonparametric reward estimation and (ii) an epoch-based design with adaptive […]

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arXiv:2601.22275v1 Announce Type: new Abstract: The quadratic complexity of the attention mechanism severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the highly sparse spatio-temporal attention patterns exhibited in Video DiTs can be naturally represented by the Monarch matrix. It is a class of structured matrices with flexible sparsity, enabling sub-quadratic attention via an alternating minimization algorithm. Accordingly, we propose VMonarch, a novel attention mechanism for Video DiTs that enables efficient computation over the […]

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arXiv:2603.00666v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware […]

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