---
license: other
license_name: arxiv-nonexclusive
license_link: https://arxiv.org/help/license
task_categories:
  - text-generation
  - feature-extraction
language:
  - en
tags:
  - mathematics
  - arxiv
  - latex
  - scientific
pretty_name: Math Papers 10M
size_categories:
  - 1K<n<10K
---

# Math Papers 10M

A curated dataset of recent mathematics papers from arXiv, prepared for
language-model training and research. Target: ~10M cl100k_base tokens of
clean body text across a balanced set of math subcategories.

## Summary

- **Total papers:** 901
- **Total body tokens (cl100k_base):** 33,121,411
- **Source:** arXiv (https://arxiv.org), via the public metadata API and
  content endpoints (`/html/`, `/e-print/`, `/pdf/`)
- **Generated:** 2026-04-12
- **License:** Papers are redistributed under arXiv's non-exclusive license
  to distribute. See https://arxiv.org/help/license. Cite the original
  authors when using this data.

## Contents

Each batch is a Parquet file under `data/`:

```
data/batch-0001.parquet
data/batch-0002.parquet
...
```

Per-row schema:

| field | type | description |
| --- | --- | --- |
| `arxiv_id` | string | Canonical arXiv identifier (e.g. `2401.12345`) |
| `title` | string | Paper title |
| `authors` | list[string] | Author names |
| `abstract` | string | Paper abstract |
| `primary_category` | string | arXiv primary subject (e.g. `math.AG`) |
| `categories` | list[string] | All arXiv subject tags |
| `published` | string | ISO-8601 submission timestamp |
| `updated` | string | ISO-8601 last-updated timestamp |
| `abs_url` | string | arXiv abstract URL |
| `pdf_url` | string | arXiv PDF URL |
| `body` | string | Cleaned body text (math preserved where possible) |
| `body_chars` | int | Character length of `body` |
| `body_tokens` | int | Token count via tiktoken `cl100k_base` |
| `extraction_method` | string | `html`, `pylatexenc`, or `pymupdf` |
| `has_tex` | bool | Whether original .tex source was retrieved |
| `has_pdf` | bool | Whether original PDF was retrieved |

Original artifacts (when available) are stored alongside the data:

```
sources/tex/<arxiv_id>.tar.gz   # original .tex tarball from arXiv
sources/pdf/<arxiv_id>.pdf      # original rendered PDF
```

## Category Breakdown

| Primary category | Papers | % |
| --- | ---: | ---: |
| math.AG | 61 | 6.8% |
| math.CO | 51 | 5.7% |
| math.LO | 47 | 5.2% |
| math.NT | 46 | 5.1% |
| math.RT | 45 | 5.0% |
| math.GT | 42 | 4.7% |
| math.PR | 41 | 4.6% |
| math.NA | 41 | 4.6% |
| math.DG | 37 | 4.1% |
| math.OC | 36 | 4.0% |
| math.FA | 32 | 3.6% |
| math.CA | 31 | 3.4% |
| math.DS | 30 | 3.3% |
| math.AT | 30 | 3.3% |
| math.RA | 30 | 3.3% |
| math.CT | 27 | 3.0% |
| math-ph | 27 | 3.0% |
| math.QA | 25 | 2.8% |
| math.ST | 24 | 2.7% |
| math.SG | 23 | 2.6% |
| math.AP | 19 | 2.1% |
| math.GR | 14 | 1.6% |
| cs.LG | 13 | 1.4% |
| hep-th | 12 | 1.3% |
| stat.ML | 10 | 1.1% |
| quant-ph | 9 | 1.0% |
| eess.SY | 8 | 0.9% |
| math.OA | 7 | 0.8% |
| cs.LO | 6 | 0.7% |
| cond-mat.stat-mech | 6 | 0.7% |
| cs.IT | 6 | 0.7% |
| math.AC | 5 | 0.6% |
| cs.DS | 5 | 0.6% |
| math.KT | 5 | 0.6% |
| stat.ME | 4 | 0.4% |
| math.SP | 4 | 0.4% |
| cs.CC | 4 | 0.4% |
| gr-qc | 3 | 0.3% |
| math.CV | 3 | 0.3% |
| physics.ao-ph | 3 | 0.3% |
| cond-mat.soft | 2 | 0.2% |
| cs.AI | 2 | 0.2% |
| math.MG | 2 | 0.2% |
| math.HO | 2 | 0.2% |
| cs.CV | 2 | 0.2% |
| cs.RO | 2 | 0.2% |
| nlin.AO | 1 | 0.1% |
| astro-ph.IM | 1 | 0.1% |
| cs.DB | 1 | 0.1% |
| physics.atom-ph | 1 | 0.1% |
| stat.CO | 1 | 0.1% |
| physics.plasm-ph | 1 | 0.1% |
| physics.data-an | 1 | 0.1% |
| nlin.PS | 1 | 0.1% |
| cs.GT | 1 | 0.1% |
| q-fin.PM | 1 | 0.1% |
| astro-ph.CO | 1 | 0.1% |
| nucl-th | 1 | 0.1% |
| astro-ph.EP | 1 | 0.1% |
| cs.CG | 1 | 0.1% |
| cs.ET | 1 | 0.1% |
| physics.flu-dyn | 1 | 0.1% |
| cond-mat.other | 1 | 0.1% |

No single subcategory exceeds 40% of the total.

## Methodology

1. **Metadata.** Queried `export.arxiv.org/api/query` for each of ~20 math
   subcategories (including `math-ph`), sorted by submission date descending,
   pulling up to 150 papers per category. Recent papers are preferred.
2. **Balanced sampling.** Papers are round-robin sampled across categories
   with a 40% cap on any single category.
3. **Content fetch.** For each sampled paper we try, in order:
   - `arxiv.org/html/<id>` — native HTML5 render with math preserved
   - `arxiv.org/e-print/<id>` — .tex source tarball
   - `arxiv.org/pdf/<id>.pdf` — rendered PDF
4. **Extraction cascade.**
   - HTML path: BeautifulSoup + lxml, stripping nav, bibliography, author
     metadata, figures, and page headers/footers.
   - TeX path: `pylatexenc` with `math_mode="with-delimiters"` after cutting
     off `\bibliography` / `thebibliography` sections.
   - PDF path: `pymupdf`, skipping the title page and the last two pages
     (references / appendices).
5. **Quality gate.** Bodies must have at least 1000 characters and 300
   cl100k_base tokens. Papers failing the cascade at every level are logged
   and excluded.
6. **Tokenization.** All token counts use `tiktoken` with the
   `cl100k_base` encoding (the encoding used by GPT-4 / GPT-3.5-turbo).
7. **Rate limiting.** All arXiv requests respect a 3-second minimum delay
   with exponential backoff on `429` / `503` responses.

## Known Limitations

- `pymupdf`-based extractions lose math structure — equations become flat
  text. The `extraction_method` field lets you filter these out if desired.
- Figures, tables, and captions are dropped from the body text in all
  extraction paths.
- Some older papers do not have HTML or TeX sources; these rely on the PDF
  fallback.
- The dataset is a snapshot — arXiv IDs and categories can be updated later
  by authors. Re-run the pipeline to refresh.

## Intended Use

Research, pretraining, and fine-tuning of models that handle mathematical
text. Not a substitute for reading the original papers.

## Citation

Please cite the original arXiv papers when using individual rows. If you
need to cite this specific extraction, use:

```
@dataset{math_papers_10m_20260412,
  title   = {Math Papers 10M},
  year    = 2026,
  url     = {https://huggingface.co/datasets/slabhead/math-papers-10m}
}
```