Rewind Before the Uncertainty Peak
TL;DR for operators When a reasoning model makes a mistake partway through an answer, restarting from the beginning discards potentially correct work, while revising only the ending may preserve the mistaken premise. The operational challenge is choosing where to restart—not simply finding where the model appears most uncertain. REWIND, a training-free inference controller, traces backward from the point of highest token uncertainty to an earlier sharp rise in uncertainty, aligns the restart with a preceding text boundary, and regenerates the remaining response.1 On Qwen2.5-Math-7B/MATH500, this combined approach achieved 77.60% accuracy, compared with 74.20% for direct revision at the uncertainty peak, 75.00% for backward tracing without boundary alignment, and 72.60% for random-cut MCMC. The improvement demonstrates more productive intervention, not verified identification of the original reasoning error. ...