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Frontier physics has long required mathematics, instruments, and large collaborations to make sense of the world. Frontier AI is raising a similar question: when capabilities grow faster than our understanding, how do we check reasoning we can no longer follow from beginning to end?
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Why can parallel prefill, token-by-token recurrent rollout, and fully parallel policy replay define different policies? A look at LastKV’s fixed chunk recurrence through probability and gradient consistency in RL.
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From Score Centering, BPO, and MaxRL to Self Forcing, video preference optimization, and KV quantization correction: understanding drift across domains through distributions, feedback loops, and points of intervention.