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  1. Huxiu

    An unreleased OpenAI model produced 722 mathematics manuscripts overnight, prompting a joint criticism from 25 Fields Medalists

    OpenAI published the openai/math repository on GitHub, disclosing 722 mathematics manuscripts and 372 result groups in one go, all produced by an undisclosed internal model, covering results such as a quasi-Riemann hypothesis, reducing the exponent of matrix multiplication over the complex field to 2.25, and a four-dimensional version of the Kakeya conjecture. The authors state that each result cost on average about 3 hours of ChatGPT Pro-level compute, whereas the September attack on the Navier–Stokes equations deployed roughly 10,000 agents, ran for 88 hours, and cost millions of dollars. 25 Fields Medalists signed a joint letter titled "The Serious Misalignment of AI in Mathematics," criticizing AI companies for treating problem-solving as a marketing tool, and the advisory group of the Institute for Advanced Study in Princeton demanded that such practices stop.

    OpenAI published the openai/math repository on GitHub, disclosing 722 mathematics manuscripts and 372 result groups in one go, all produced by an undisclosed internal model, covering results such as a quasi-Riemann hypothesis, reducing the exponent of matrix multiplication over the complex field to 2.25, and a four-dimensional version of the Kakeya conjecture. The authors state that each result cost on average about 3 hours of ChatGPT Pro-level compute, whereas the September attack on the Navier

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    #hot-signal#English text is being prepared.#OpenAI#Papers/Research

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Tuesday

  1. The Decoder

    Researchers extend LeCun's JEPA into a general-purpose world model spanning physics to biology

    PhAI Labs, together with Stanford, Oxford, and Princeton, proposed JEPA-Anything, which splits JEPA's single prediction into multiple specialized prediction modules and lets each module differentiate into distinct roles during training, thereby covering seven domains—including physics, robotics, weather, single-cell, and clinical—with a single architecture. In a simplified Pong environment, prediction error dropped 35%, and on unseen intervention combinations it dropped 13%; on the Burgers equation benchmark the error was nearly halved, but after 50 steps the advantage shrank to about 3%; in simulated walking-robot planning, it won two of three environments. The team also screened biological data to identify a candidate liver cancer regimen combining IL-18 with CD73 blockade, and tested it in organoids, patient tissue, and mice; the paper also notes that this does not yet show it can become a therapy, and the code and models have been made public.

    PhAI Labs, together with Stanford, Oxford, and Princeton, proposed JEPA-Anything, which splits JEPA's single prediction into multiple specialized prediction modules and lets each module differentiate into distinct roles during training, thereby covering seven domains—including physics, robotics, weather, single-cell, and clinical—with a single architecture. In a simplified Pong environment, prediction error dropped 35%, and on unseen intervention combinations it dropped 13%; on the Burgers equat

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    #ai-vertical#Papers/Research#Multimodal#English text is being prepared.