Human and AI-generated texts between modal logic and statistics
Read the original on arXiv AI →The paper examines the geometry of semantic neighbourhood graphs as modal logic, translating this view into a statistical framework to differentiate human from AI-generated text. It models texts as worlds in a finite frame where accessibility is defined by the k‑nearest‑neighbour relation of transformer embeddings, and measures the frequencies of modal axioms (B, 4, 5, D) as validation degrees. A prompt‑balanced comparison shows consistently higher degrees for axioms 4 and 5 in AI‑generated corpora, and the study further introduces measures of groundedness and situatedness, recasting the analysis in a sequent‑style tableau setting. "whyItMatters":"The work provides a quantitative, modal‑logic‑based method to detect structural differences between human and machine‑generated text, offering a new lens for evaluating AI language models."
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