arXiv paper: Why is AI-generated language hard to understand?

arXiv paper 2610.29162 studies why AI-generated text is harder to understand: compared with human writing, all 9 frontier and near-frontier models more frequently use inanimate subjects paired with animate verbs and low-frequency n-grams, and most models also use longer dependencies, more noun stacking, and reduced object relative clauses, with clear differences among model families. The authors propose that human language is optimized for successful communication, whereas AI text is increasingly optimized for information-density objectives such as math, code, and reasoning; fine-tuning language models under a length penalty can reproduce behavior that deviates from human language, providing a proof of concept for this hypothesis.