Large language models exhibit early belief drift in multiple-choice question answering, violating the martingale property. Prompted predictive resampling (PPR) reveals this drift, which self-stabilizes after sufficient resampling, leading to coherent predictive distributions. We propose a seed-answer prompting strategy and a self-consistency loss to accelerate stabilization and reduce drift, improving predictive coherence without affecting accuracy.