Writing Style Similarity Reflects Academic Genealogy
Quick summary
arXiv:2608.14843v1 Announce Type: cross Abstract: As authorship attribution systems are increasingly deployed to detect ghostwritten and AI-generated papers, their errors can support accusations against legitimate authors. These systems assume each author's style is their own. Researchers, however, study under advisors, and inherit their stylistic quirks. We build a corpus of arXiv authors with $\geq 2$ 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
Key takeaways
- arXiv:2608.14843v1 Announce Type: cross Abstract: As authorship attribution systems are increasingly deployed to detect ghostwritten and AI-generated papers, their errors can support accusations against legitimate authors.
- These systems assume each author's style is their own.
- Researchers, however, study under advisors, and inherit their stylistic quirks.
Why it matters
“Writing Style Similarity Reflects Academic Genealogy” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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