Writing Style Similarity Reflects Academic Genealogy

As authorship attribution systems are increasingly deployed to detect ghostwritten and AI-generated papers, their errors can support accusations against legitimate authors. These systems conflate stylistic similarity with individual identity. Researchers, however, study under advisors, and inherit their stylistic quirks. We build a corpus of arXiv authors with at least 2 solo papers from the Mathematics Genealogy Project graph, giving 5, 803 total authors and 2, 501 ground-truth advisor-student pairings. Using embeddings from a fine-tuned model, advisors sit 39.9% closer in cosine distance to their students than a random same-field author does. Using two open models, we reproduce the effect at 12.6% and 14.5%. Academic siblings, two students of one advisor who may never have met, sit 30.4% closer across 8, 360 pairs, even when they studied at different institutions. Pairs who share only institution and field show negligible similarity. Given a closed-set attribution task over the same corpus, the system's errors occur on the true author's advisor, student, academic sibling, or lab mate 11 times more often than chance.