LA-CPD: Local-Evidence-Aware Change-Point Detection for Human–LLM Authorship Segmentation

As LLM-generated text becomes increasingly human-like, failing to localize LLM-authored spans in human–LLM co-authored documents may weaken our ability to establish accountability for copyright infringement, fraud, and other harmful uses of AI-generated content. Existing sentence-level detection scores provide local evidence for these transitions, but content differences can cause score fluctuations even among sentences from the same source, potentially intro- ducing spurious boundaries. Identifying genuine authorship transitions and delin- eating the corresponding document partition therefore remain challenging when both boundary locations and counts are unknown. We propose **L**ocal-**E**vidence- **A**ware **C**hange-**P**oint **D**etection (**LA-CPD**), a structured method that converts noisy score sequences into coherent authorship segments. Given scores from a frozen local detector, LA-CPD combines a length-weighted within-segment resid- ual with a windowed two-mean contrast to capture both segment consistency and sustained changes across candidate cuts. Dynamic programming optimizes cut locations for each candidate count, while an AIC-style criterion selects the fi- nal partition for delineating sentence labels, authorship boundaries, and maximal LLM-authored spans. Experiments on a held-out human–LLM co-authored test set show that LA-CPD outperforms WCP+AIC, improving sentence-level accu- racy from 0.747 to 0.796 while achieving better boundary localization and LLM- span delineation.