# EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces

arXiv: https://arxiv.org/abs/2603.28515 · HTML: https://mlsanity.com/paper/2603.28515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [NLPeer: A Unified Resource for the Computational Study of Peer Review](https://mlsanity.com/paper/2211.06651/) (2022)
- [Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision](https://mlsanity.com/paper/2204.03685/) (2022)
- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [MOPRD: A multidisciplinary open peer review dataset](https://mlsanity.com/paper/2212.04972/) (2022)
- [arXivEdits: Understanding the Human Revision Process in Scientific Writing](https://mlsanity.com/paper/2210.15067/) (2022)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance](https://mlsanity.com/paper/1910.09180/) (2019)
- [A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication](https://mlsanity.com/paper/1405.1439/) (2014)
- [CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions](https://mlsanity.com/paper/2403.00241/) (2024)
- [Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision](https://mlsanity.com/paper/2406.00197/) (2024)
- [ParaRev: Building a dataset for Scientific Paragraph Revision annotated with revision instruction](https://mlsanity.com/paper/2501.05222/) (2025)
- [*-PLUIE: Personalisable metric with Llm Used for Improved Evaluation](https://mlsanity.com/paper/2602.15778/) (2026)

## Built on by (most cited first, 1 of 1)

- ["Nothing to See Here'': Unintended Disclosure through Revision Traces of LLM Deliverables](https://mlsanity.com/paper/2609.35408/) (2026)

## Abstract (arXiv)

Scientific writing is an iterative process that generates rich revision traces, yet publicly available resources typically expose only final or near-final versions of papers. This limits empirical study of revision behaviour and evaluation of large language models (LLMs) for scientific writing. We introduce EarlySciRev, a dataset of early-stage scientific text revisions automatically extracted from arXiv LaTeX source files. Our key observation is that commented-out text in LaTeX often preserves discarded or alternative formulations written by the authors themselves. By aligning commented segments with nearby final text, we extract paragraph-level candidate revision pairs and apply LLM-based filtering to retain genuine revisions. Starting from 1.28M candidate pairs, our pipeline yields 578k validated revision pairs, grounded in authentic early drafting traces. We additionally provide a human-annotated benchmark for revision detection. EarlySciRev complements existing resources focused on late-stage revisions or synthetic rewrites and supports research on scientific writing dynamics, revision modelling, and LLM-assisted editing.