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The research team proposed "adversarial speculative reasoning": first identify the decision points crucial to the outcome in the agent's reasoning trajectory, then train the draft model to form an adversarial preference, making the draft model diverge from the target model at these key points, thereby triggering verification restarts and letting key thinking tokens be resampled and encoded by the target model under conditions of minimal noise.
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11/4/2026
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