Reason Forward, Reflect When Needed: Predictive Anchoring with Suffix-Evidence for Revisable LLM Reasoning

Human experts seldom revise uncertain intermediate steps at once. They tentatively accept a step, retain alternatives, inspect downstream effects, and revise only evidence-supported parts of the reasoning state. Existing latent-reasoning methods, however, typically activate continuous representations at high-entropy tokens, conflating uncertainty with the need for latent reasoning. Our analysis reveals three findings: Uncertainty is immediately observable but insufficient for intervention. Intervention impacts emerge later yet locally diagnosable. Revision scope should match influence scope. Building on these observations, we propose PASER, a training-free LLM reasoning framework with three key innovations: 1) PASER treats high-entropy uncertainty as a signal for caching predictive anchors, rather than immediately activating latent reasoning. 2) PASER restores each anchor and evaluates the same realized suffix under discrete CoT and predictive soft histories, determining whether the current latent reasoning interference has an effect. 3) By accumulating impact evidence over a longer horizon, PASER maps its strength to hierarchical reflective actions. Evaluated on mathematical, STEM, coding, and general reasoning benchmarks, PASER achieves aggregate performance over competitive training-free baselines across diverse model families and scales.