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Chain-of-thought is far more than surface imitation: LLMs carve out discrete, context-dependent geometric subspaces for functional operations like deduction and decomposition, with middle layers encoding the abstract reasoning step rather than the literal tokens.
Speculative decoding can be significantly accelerated by training models to anticipate verification outcomes, leading to faster and more efficient inference.
Imagine a world model that doesn't just dream up environments, but flawlessly renders a real city like Seoul, complete with text-prompted scenarios and diverse camera movements.
