PEC-Detector: Planning-Execution Coupling Trajectories for Document-Level LLM-Generated Text Detection

Reliable AI-generated text detection is increasingly difficult as large language models (LLMs) produce fluent and coherent long texts. Existing detectors typically derive document-level evidence from static textual representations or aggregated statistics, without explicitly modeling dynamic coupling between discourse planning and current writing execution as a document unfolds. We show that remote discourse history provides measurable conditional information about the current sentence beyond local context, and that its contribution follows different trajectories in machine-generated text (MGT) and human-written text (HWT). Motivated by this observation, we propose **PEC-Detector**, a planning-execution coupling framework for document-level machine-generated text detection. At each sentence position, we induce planning and local context states from remote discourse history and local context, respectively, and construct a conditional planning-execution relation with the current writing execution. The resulting position-wise representations form an ordered coupling trajectory for document-level source prediction. A joint conditional semantic prediction objective encourages the planning state to retain information relevant to current writing execution. Experiments on public benchmarks spanning diverse domains, generators, and languages show that our method consistently achieves state-of-the-art detection performance. Furthermore, PEC-Detector remains robust under distribution shifts and adversarial attacks.