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LongCat-Next shatters the language-centric paradigm by unifying text, vision, and audio into a single autoregressive model with minimal modality-specific design, finally reconciling understanding and generation in discrete vision modeling.
A new shuffle-DP protocol achieves 10x better utility and 3x better robustness against data poisoning for distribution estimation, all while using a single message per user.
