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2026-03-25

5 saved articles

  1. TurboQuant: Redefining AI efficiency with extreme compression

    research.google

    Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache, a high-speed "digital cheat sheet" that stores frequently u

  2. LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

    [Submitted on 13 Mar 2026 (v1), last revised 3 Jun 2026 (this version, v3)] · arXiv.org

    Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse. In this work, we introduce LeWorldModel (LeWM), the first JEPA that trains stably end-to-end from raw pixels using only two loss terms: a next-embedding prediction loss and a regularize

  3. Your bridge to wealth is being pulled up

    Approaching 50% · Daniel Homola

    For two centuries, the credential system gave intelligence a route to heritable capital. Artificial intelligence is closing that route. This essay builds the argument from first principles - with probability theory, interactive simulations, and a prediction specific enough to be falsifiable - and puts a number on the window that remains.