Abstract
Large language models (LLMs) need reliable test-time control of hallucinations. Existing conformal methods for LLMs typically provide only *marginal* guarantees and rely on a single global threshold, which can under-cover hard prompts, over-cover easy ones, and produce oversized prediction sets. We propose *Conditional Factuality Control* (CFC), a post-hoc conformal framework that returns *set-val