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

• URL: http://arxiv.org/abs/2603.28515v1
• Date: Mon, 30 Mar 2026 14:47:04 GMT
• Title: EarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces
• Abstract summary: We introduce EarlySciRev, a dataset of early-stage scientific text revisions automatically extracted from arXiv source files.<n>Our key observation is that commented-out text in often preserves discarded or alternative formulations written by the authors themselves.<n>Our pipeline yields 578k validated revision pairs, grounded in authentic early drafting traces.
• Score: 5.062670468960367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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