When to Flag, Where to Localize: Risk-Controlled Detection with Authorship Localization in Mixed Human–LLM Text

Documents containing both human-written and LLM-generated text require both deciding whether LLM-generated text exists and identifying where it occurs. Localization methods can in principle address both by treating any predicted LLM segment as evidence that the document contains generated text, but sentence-level errors can then produce false alarms on fully human-written documents. We therefore propose CoMa-LD, a training-free framework for sequential LLM detection and post-alarm localization. CoMa-LD introduces a two-state Human/LLM Markov model into a conformal test martingale: summing evidence over candidate authorship paths determines when to flag, while selecting the highest-weight path identifies where the generated text occurs. The resulting detector provides statistical guarantees that control the probability of ever falsely flagging a fully human-written document. Across multiple domains and authorship transition patterns, CoMa-LD improves both detection and localization over the evaluated baselines while maintaining low false-alarm rates. Our code is available at https://anonymous.4open.science/r/CoMa-LD-B486/.