Writing Style Similarity Reflects Academic Genealogy

# Writing Style Similarity Reflects Academic Genealogy
Source: [https://arxiv.org/html/2608.14843](https://arxiv.org/html/2608.14843)
###### 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≥2\\geq 2solo papers from the Mathematics Genealogy Project graph, giving5,8035\{,\}803total authors and2,5012\{,\}501ground\-truth advisor\-student pairings\. Using embeddings from a fine\-tuned model, advisors sit39\.9%39\.9\\%closer in cosine distance to their students than a random same\-field author does\. Two open encoders reproduce the effect at12\.6%12\.6\\%and14\.5%14\.5\\%\.*Academic siblings*, two students of one advisor who may never have met, sit30\.4%30\.4\\%closer across8,3608\{,\}360pairs, even when they studied at different institutions\. Pairs who share only an institution and a field show negligible similarity\. Given a closed\-set attribution task over the same corpus, the system’s errors occur on the true author’s advisors and academic siblings1111times more often than chance\.

## 1Introduction

When an attribution system flags a student’s paper as written by their advisor, the false positive is difficult to disprove during peer review\. Whether such systems systematically produce these false accusations depends on how much of a researcher’s writing style comes from mentors\.

Advisors and students share subfields, departments, and native languages\. Each factor inflates stylistic similarity independently of mentorship\. Therefore, we control for shared fields and institutions to discover a true effect\. Furthermore, these relationships should be verifiable rather than being inferred, which existing measurements of advisor influence lack \(Section[2](https://arxiv.org/html/2608.14843#S2)\)\. To address this limitation, we build our corpus from the Mathematics Genealogy Project \(MGP\) graph, which has been curated for ground truth\.

Our contribution is a ground\-truth based measurement of academic genealogy in writing style\. We link curated MGP records to authorship\-attribution embeddings, creating 2,501 advisor\-student pairs and 14,398 total sibling pairs \(including 8,360 across different institutions\)\. To our knowledge, we provide the first stylistic analysis of academic siblings\. We use these results to show how this effect causes significant failures in closed\-set attribution tasks\.

## 2Related Work

#### Writing style dynamics\.

[Lazebnik and Rosenfeld 2025](https://arxiv.org/html/2608.14843#bib.bib3)modeled individual scholarly style evolution across 13\.7M publications, finding that trajectories stabilize around publication 13 and that advisor influence is measurable in early\-career work\. Advisors there are inferred as a scholar’s most frequent co\-author in their first three publication years, and the analysis reaches no further than the direct advisor link\.[Danescu\-Niculescu\-Mizil et al\. 2011](https://arxiv.org/html/2608.14843#bib.bib1);[Danescu\-Niculescu\-Mizil et al\. 2012](https://arxiv.org/html/2608.14843#bib.bib2)established that speakers converge toward the style of high\-status interlocutors in online communities, providing a theoretical mechanism for the advisor effect we measure\. We extend both lines by measuring against curated genealogy records and past the direct link to academic siblings\.

#### Academic genealogy\.

Studies of the MGP\([David et al\. 2022](https://arxiv.org/html/2608.14843#bib.bib6)\)have traced how research topics propagate through advisor lineages\.[Malmgren et al\. 2010](https://arxiv.org/html/2608.14843#bib.bib4)showed that mentorship structure shapes productivity trajectories in physics, with an advisor’s network position predicting a student’s long\-run output patterns\. Both treat the genealogy graph as a channel for transmitting career\-level properties; we ask whether style is transmitted through the same channel\.

#### Authorship attribution\.

Authorship attribution systems map text to author embeddings and identify authors by nearest\-neighbor search in embedding space\([Stamatatos et al\. 2024](https://arxiv.org/html/2608.14843#bib.bib5)\)\. Purpose\-built encoders such as LUAR\([Rivera\-Soto et al\. 2021](https://arxiv.org/html/2608.14843#bib.bib7)\)and content\-independent models such as StyleDistance\([Patel et al\. 2024](https://arxiv.org/html/2608.14843#bib.bib8)\)represent the state of the art\. These systems are built on the implicit assumption that each author’s style is independent—that knowing one author’s style tells you nothing about another’s\. We challenge that assumption using social structure data\.

## 3Data and Methods

#### Corpus\.

We crawled 5,715 Mathematics Genealogy Project pages, yielding 6,133 eligible people \(those with≥\\geq2 single\-authored arXiv papers\), of whom 5,803 have text and embeddings\. We control date to pre\-2024 to avoid large changes in style from proliferation of LLMs\. Sole\-author papers were used throughout so embeddings reflect a ground\-truth individual style\. Author text is the*abstracts*of each author’s sole\-authored papers, taken from the arXiv metadata snapshot \(mean 5\.7 papers per author, median 4\); affiliation strings were stripped before embedding to prevent literal string overlap from inflating same\-institution similarity\.

#### Embeddings\.

Our main encoder \(henceforth “the tuned encoder”\) fine\-tunes a Mistral\-based model for authorship attribution following[Kandula et al\. 2025](https://arxiv.org/html/2608.14843#bib.bib9)\. Weights and training data are not released\. Chunk\-level embeddings \(1,536\-dim\) are mean\-pooled to paper level, then to author level\. We use cosine distance as our similarity metric\.

#### Institution assignment\.

We consider institution to be each person’s PhD\-granting school as recorded on their MGP page\. This records where an author*received their PhD*, not where they currently work\. We yield 3,609 people \(59%\) across 459 unique institutions\.

#### advisor\-student pairs\.

Relationships are read directly from the MGP graph by numeric page ID\. When resolving a person to their arXiv record, we resolve on exact normalized full name and*discard*any name claimed by two different genealogy people \(230 dropped\)\. This yields 2,771 advisor\-student pairs and 15,936 sibling pairs, of which 2,501 and 14,398 respectively have embeddings for both members and form the analysis samples\. Sibling pairs are split by whether the two members trained at the same institution \(6,038\) or different institutions \(8,360\)\. Only the different\-institution subset supports a lineage claim independent of shared environment, and we treat it as the headline sibling result throughout\. A subset of 1,286 advisor pairs is analysed by career phase in Appendix[D](https://arxiv.org/html/2608.14843#A4)\.

#### Statistical validation\.

We shuffle the relationship label \(advisor or institution\) within each field and recompute the mean distance;pp\-values are the fraction of 1,000 shuffles at or below the observed value \(10,000 for the lab\-mate test\)\.

#### Open model replication\.

We replicate under LUAR\([Rivera\-Soto et al\. 2021](https://arxiv.org/html/2608.14843#bib.bib7)\)\(intended for authorship representation\) and StyleDistance\([Patel et al\. 2024](https://arxiv.org/html/2608.14843#bib.bib8)\)\(content\-independent\)\.

## 4Results

### 4\.1Lab\-Mate Similarity

Once advisor\-student and sibling links are removed, shared PhD institution on its own does not produce a reliable stylistic effect\. Under the author\-level permutation test, the pooled same\-institution mean sits 3\.2% below its permutation null under the tuned encoder across 57,250 pairs among 3,083 people\. We find LUAR gives−1\.0%\-1\.0\\%\(p=1\.000p=1\.000\) and StyleDistance−0\.6%\-0\.6\\%\(p=0\.997p=0\.997\)\. On the normalized scale \(Table[3](https://arxiv.org/html/2608.14843#A1.T3)\) the tuned\-encoder figure is 5\.0% of the way from a random stranger toward the same author, against 61\.7% for advisors\.

Table 1:Writing similarity by relationship, under the tuned encoder, all measured against the same random pairing baseline\. %cl = % closer than random pair\. For same\-person value we use the distance between two halves of the same author’s papers and is the 0 point of Table[3](https://arxiv.org/html/2608.14843#A1.T3)
### 4\.2Advisor\-Student Similarity

The permutation test givesp<0\.001p<0\.001for all three encoders\. Both open models agree with the tuned encoder in direction and significance at the pooled level, at smaller magnitude: 39\.9% under the tuned encoder against 12\.6% under LUAR and 14\.5% under StyleDistance\. Comparing embedding spaces is noisy, so we also find a normalized scale \(Table[3](https://arxiv.org/html/2608.14843#A1.T3)\), on which the tuned encoder and StyleDistance agree closely \(61\.7% and 62\.7% of the way toward same\-person\)\.

Table 2:Relationship distance vs\. random same\-field baseline\. For advisor\-student, we are field\-agnostic, e\.g\., an advisor can publish in a different field than their student\. %cl = % closer than null\.
### 4\.3Academic\-Sibling Similarity

Two students of the same advisor write more similarly than chance under all three encoders \(Table[2](https://arxiv.org/html/2608.14843#S4.T2)\)\. Siblings have no verified connection to each other, and frequently no temporal overlap in the group at all\. Therefore, we deduce their shared style originates from a shared mentor\.

One may object that siblings are usually trained in the same department\. However, splitting the pairs, we find siblings who trained at the*same*institution sit 34\.2% below null and siblings who trained at*different*institutions sit 30\.4% below null, a gap of under four points\. Both open models show this trend \(see Table[3](https://arxiv.org/html/2608.14843#A1.T3)\)\.

### 4\.4Effect on an Attribution System

By themselves, the similarity results do not establish implications for authorship attribution systems\. We therefore run a closed\-set attribution task and log the errors it makes\.

For every author in our corpus with at least four papers, we withdraw one paper, build their profile from the remainder, and rank all candidate authors by cosine distance to the withdrawn paper\. Pools are restricted to the same primary arXiv category\. With the tuned model, over 2,881 authors, the system ranks the true author first 45\.9% of the time and makes 1,559 errors\.

In 125 of these 1,559 cases \(8\.02%\) the author the system picks is a social relative of the true author, which we consider an advisor, a student, an academic sibling, or from the same PhD institution\. Uniformly distributed errors would produce this 0\.73% of the time, so this result occurs11\.0×\\timesmore often than chance \(p=6\.1×10−85p=6\.1\\times 10^\{\-85\}\)\. This is the same direction within fields in the corpora\. For those with usable size: cs 16\.6×\\times, math 11\.9×\\times, cond\-mat 10\.0×\\times, math\-ph 7\.8×\\times, hep\-th 1\.8×\\times\.

## 5Discussion

Our results indicate mentorship leaves a measurable trace in writing style, while shared institution alone does not\. This discrepancy in similarity between advisors as well as siblings has the implications to confuse authorship attribution systems at rates a magnitude greater than chance\.

The decay of the advisor signal during an author’s career \(Appendix[D](https://arxiv.org/html/2608.14843#A4)\) is consistent with stylistic imprinting from advisors\. Even if students choose advisors who write similar to them initially, it cannot explain why this similarity decreases over time, characteristic of an independent voice emerging\.

Siblings need not have met or even publish in the same field, ruling out co\-authorship or shared lexicon\. Hence, a common advisor remains the clearest explanation for why they write so similarly\.

#### Limitations\.

*Field diversity:*because MGP originates from math, the corpus is 71% math, 8% cs, 4% hep\-th, 2% stat, with a remainder in 40 other arXiv categories\.

*Subfield clustering and shared linguistic background:*topic confounds are bounded by StyleDistance’s content\-independent training and the cross\-field check \(Appendix[C](https://arxiv.org/html/2608.14843#A3)\)\. Appendix[B](https://arxiv.org/html/2608.14843#A2)shows linguistic background contributes 18\.1%, which we cannot parse from mentorship\.

## References

- Danescu\-Niculescu\-Mizil et al\. \(2011\)C\. Danescu\-Niculescu\-Mizil, M\. Gamon, and S\. Dumais\.Mark my words\! Linguistic style accommodation in social media\.In*Proceedings of WWW*, 2011\.
- Danescu\-Niculescu\-Mizil et al\. \(2012\)C\. Danescu\-Niculescu\-Mizil, L\. Lee, B\. Pang, and J\. Kleinberg\.Echoes of power: Language effects and power differences in social interaction\.In*Proceedings of WWW*, 2012\.
- Lazebnik and Rosenfeld \(2025\)T\. Lazebnik and A\. Rosenfeld\.A computational model for individual scholars’ writing style dynamics\.*Journal of Writing Research*, 17\(1\), 2025\.
- Malmgren et al\. \(2010\)R\. D\. Malmgren, J\. M\. Ottino, and L\. A\. N\. Amaral\.The role of mentorship in protégé performance\.*Nature*, 465\(7298\):622–626, 2010\.
- Stamatatos et al\. \(2024\)E\. Stamatatos et al\.Overview of the multi\-author writing style analysis task at PAN 2024\.In*CLEF 2024 Working Notes / Springer LNCS*, 2024\.
- David et al\. \(2022\)S\. David et al\.Academic genealogy and the flow of knowledge\.*Scientometrics*, 2022\.
- Rivera\-Soto et al\. \(2021\)R\. A\. Rivera\-Soto, O\. E\. Miano, J\. Ordonez, B\. Y\. Chen, A\. Khan, M\. Bishop, and N\. Andrews\.Learning universal authorship representations\.In*Proceedings of EMNLP*, 2021\.
- Patel et al\. \(2024\)A\. Patel, Z\. Horvitz, A\. Alyafeai, K\. McKeown, and S\. Muresan\.StyleDistance: Stronger content\-independent style embeddings with synthetic parallel examples\.*arXiv preprint arXiv:2410\.12757*, 2024\.
- Kandula et al\. \(2025\)H\. Kandula, C\. F\. Li, H\. Qiu, D\. Karakos, H\. Man, T\. H\. Nguyen, and B\. Ulicny\.BBN\-U\.Oregon’s ALERT system at GenAI content detection task 3: Robust authorship style representations for cross\-domain machine\-generated text detection\.In*Proceedings of the 1st Workshop on GenAI Content Detection \(GenAIDetect\)*, pages 358–364, 2025\.

## Appendix ANormalized Effect\-Size Scale

Table 3:Effect size on a scale anchored at 0 by an author’s own split\-half distance and 1 by a random same\-field author\. LUAR gives no per\-document vector, so its floor is undefined for it\.
## Appendix BNegative Control: Name\-Collision False Matches

We evaluate 2,712 unrelated same\-field author pairs who share a surname using the same pipeline\. As a result, authors who share surnames sit 18\.1% closer in style than random pairs \(p<0\.001p<0\.001\)\. Surnames are a decent proxy for linguistic background, which shapes English writing style\. However, the effect with advisors from the same field \(43\.2%\) and different\-institution siblings \(30\.4%\) are much larger than the surname effect \(18\.1%\)\. Regardless, we report this phenomenon as a limitation\.

## Appendix CCross\-Field Robustness Check

The 473 cross\-field advisor pairs, who share no subfield by definition, sit 26\.7% below their null \(p<0\.001p<0\.001\), against 43\.2% for same\-field pairs\. The effect is weaker, but still sizable and significant\. If the phenomenon were purely topic\-driven, this effect would not occur\.

## Appendix DCareer\-Phase Analysis

Using the 1,286 pairs whose student has at least four dated papers, we sort each student’s papers by arXiv date and pool the earliest half into an early\-career profile and the latest half into a late\-career profile, then for each find the cosine distance against the advisor’s profile\.

We find early\-career mean distance is 0\.0526 and late\-career mean is 0\.0563, a change of\+7\.1%\+7\.1\\%\. This refutes the idea that students choose advisors who already write similar to themselves and, rather, supports the idea that advisors, who are early\-career to researchers, implicitly impose their style upon their mentees\.