Writing Style Similarity Reflects Academic Genealogy
Quick summary
arXiv:2608.14843v2 Announce Type: replace-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 conflate stylistic similarity with individual identity. 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
Key takeaways
- arXiv:2608.14843v2 Announce Type: replace-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 conflate stylistic similarity with individual identity.
- 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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