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The Verbalized Particle Posterior (VPP) is proposed, which treats verbalized learning as a Bayesian inference problem: maintain a population of natural-language hypotheses as particles, update them with Metropolis-Hastings or Sequential Monte Carlo, and predict by Bayesian model averaging.
A write-time admission gate that, before committing a candidate fact m extracted from context c, queries the LLM K times for a soft support score and admits m only when the average exceeds a threshold, and reduces to a single forward pass in a log-probability variant for latency-sensitive deployments.
Conformal Cascade (CC), a multi-tier inference framework that uses conformal prediction set size as the deferral rule: accept when the calibrated set collapses to a single answer, defer otherwise, delivers a distribution-free, finite-sample accuracy guarantee.
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