arxitex 0.2.1

Library for parsing arXiv papers into structured statements

A Python library for parsing arXiv papers.

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Overview

arXiTeX parses arXiv papers. Given an arXiv ID or a local LaTeX source directory, it extracts structured mathematical statements, proofs, bibliography entries, and document preambles from the raw .tex source.

It is designed for building math datasets, theorem search indices, and any downstream task that needs structured access to arXiv content.

• Paper: https://arxiv.org/abs/2602.05216
• Demo: https://huggingface.co/spaces/uw-math-ai/theorem-search

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Installation

pip install arxitex

For the "llm" method, add the extra: pip install "arxitex[llm]".

(Or install the latest from source: pip install git+https://github.com/uw-math-ai/arXiTeX.git.)

The "tex" method needs a TeX engine on your PATH — tectonic (recommended, a single self-contained binary) or a pdflatex from an existing TeX Live/MiKTeX install. The "regex" method has no system dependencies.

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Usage

Catalog arXiv paper metadata

paper_catalog streams metadata for arXiv papers in batches, filtered by category. It uses the arXiv Kaggle dataset and enriches each paper with citation counts and reference IDs via Semantic Scholar.

import arxitex as arx

for batch in arx.paper_catalog(
    download_dir="data/", # where to cache the Kaggle metadata ZIP
    categories=["math", "cs.LG"],
    batch_size=100,
):
    for paper in batch:
        print(paper.arxiv_id, paper.title)

Parameters

Parameter	Type	Default	Description
download_dir	Path | str	—	Directory for the cached arxiv.zip metadata file. Downloaded automatically on first run.
categories	List[str]	math + CS categories	Category filter. Accepts full names (math.AG) or prefixes (math).
batch_size	int	100	Papers per yielded batch.

For citation enrichment, set a Semantic Scholar API key in your environment:

SEMANTIC_SCHOLAR_API_KEY=...

Without it, citation data is unavailable — reduce batch_size to avoid rate-limiting.

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Parse a paper's statements

Everything goes through one object: Parser. Configure how to parse once, then call .parse(...) on any number of papers. A paper source can be an arXiv ID (downloaded automatically), a local path, or an s3:// URI — auto-detected.

import arxitex as arx

parser = arx.Parser(method="tex") # real TeX engine; macros/packages expanded natively

result = parser.parse("2109.06451") # arXiv ID
# OR result = parser.parse("path/to/paper/") # a local directory or .tex file
# OR result = parser.parse(s3_uri="s3://bucket/p.tar.gz")

print("parsed with:", result.method_used)
for stmt in result.statements:
    print(stmt.kind, stmt.ref, "-", stmt.note)
    print(stmt.body)
    if stmt.proof:
        print(stmt.proof)

Methods

There are three parsing methods, from fastest to most capable:

Method	How it works	Needs	Best for
"regex"	Pattern-matches environments and expands simple macros.	nothing	speed, huge corpora, no system deps
"tex"	Runs a real TeX engine over an instrumented copy of the paper, so packages, \newtheorem numbering, conditionals, and \inputs all resolve natively.	a TeX engine on PATH	accuracy on arbitrary packages/macros
"llm"	Sends section-aware chunks to an LLM and merges the extracted statements.	pip install 'arxitex[llm]' + API key	messy sources, semantic edge cases

The "tex" method uses tectonic by default (a single self-contained binary that fetches only the packages a paper needs). To use an existing TeX Live install instead, pass Tex(engine="pdflatex"). The "llm" method is provider-agnostic via litellm: pass any model string, e.g. Llm(model="anthropic/claude-sonnet-5") or Llm(model="openai/gpt-4o"), with the provider's API key in the environment (or api_key=...).

import arxitex as arx

arx.Parser(method="regex") # fast, no deps
arx.Parser(method=arx.Tex(engine="pdflatex")) # real TeX via local TeX Live
arx.Parser(method=arx.Llm(model="openai/gpt-4o")) # LLM extraction

Fallback chains

Pass a list of methods to try each in order until one yields statements — useful across a large, heterogeneous corpus. result.method_used tells you which one produced the output.

# Try the real TeX engine; fall back to regex if it's unavailable or fails.
parser = arx.Parser(method=["tex", "regex"]) # this is the default

Parameters (Parser(...))

Parameter	Type	Default	Description
method	str | Method | list	("tex", "regex")	"regex", "tex", "llm", a configured Tex(...)/Llm(...), or a list forming a fallback chain.
kinds	Set[str]	broad preset	Statement types to keep.
focus	ParseFocus | str	"all"	Which parts of the paper to populate.
validation	ValidationLevel | str	"paper"	"paper" (validate whole parse), "statement" (drop invalid ones), or "none".
context	int	0	Characters of surrounding text before/after each statement. regex only.
timeout	int	None	Max seconds per .parse() call.

.parse(...) takes the source positionally (auto-detected) or as exactly one of arxiv_id=, path=, or s3_uri=.

kinds defaults to:

{
    "theorem", "lemma", "proposition", "corollary",
    "definition",
    "axiom", "postulate",
    "conjecture", "hypothesis",
    "proof",
    "remark", "note", "observation",
    "claim", "fact", "assumption",
    "notation", "convention",
}

focus controls which fields are populated in the returned ParseResult, so you can skip work you don't need:

arx.Parser(focus="statements").parse("2109.06451") # skip preamble + bibliography
arx.Parser(focus="preamble").parse("2109.06451") # just the LaTeX preamble
arx.Parser(focus="bibliography").parse("2109.06451") # just the bibliography

focus	statements	preamble	bibliography
"all"	✓	✓	✓
"statements"	✓
"preamble"		✓
"bibliography"			✓

Command line

Installing the package also provides an arxitex CLI:

arxitex 2109.06451 -o statements.jsonl
arxitex path/to/paper/ -m tex -m regex  # fallback chain
arxitex 2109.06451 -m tex --engine pdflatex
arxitex 2109.06451 -m llm --model openai/gpt-4o

---

Parse a paper's bibliography

parse_bibliography extracts bibliography entries from a paper's source. Supports BibTeX (.bib), biblatex .bbl, amsrefs .bbl, and inline \bibitem entries.

import arxitex as arx

bibliography, is_bibtex = arx.parse_bibliography(arxiv_id="2109.06451")

for cite_key, entry in bibliography.items():
    print(cite_key, entry.get("title"), entry.get("arxiv_id"))

Returns a (dict, bool) tuple. The dict maps cite keys to metadata dicts (containing title and arxiv_id where found). The bool is True when the source was a .bib file.

Parameters

Parameter	Type	Default	Description
arxiv_id	str	None	arXiv ID. Either this or paper_path is required.
paper_path	Path | str	None	Path to a source directory. Either this or arxiv_id is required.
labels	List[str]	None	Restrict output to these cite keys. Returns all entries when None.

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Data models

ArXivPaper

class ArXivPaper(BaseModel):
    arxiv_id: str
    title: str
    authors: List[str]            # formatted as "First Middle Last"
    url: str
    categories: List[str]         # categories[0] is the primary category
    updated_at: datetime
    journal_ref: Optional[str]
    doi: Optional[str]
    license: Optional[str]        # stored as a URL when available
    abstract: str
    citation_count: Optional[int]
    reference_ids: List[str]

Statement

class Statement(BaseModel):
    kind: str                   # e.g. "theorem", "lemma", "proof"
    ref: Optional[str]          # numbering as it appears in the document, e.g. "1.1"
    note: Optional[str]         # optional title or caption
    label: Optional[str]        # LaTeX \label{...} key
    body: str                   # LaTeX body, user macros expanded where possible
    proof: Optional[str]        # raw LaTeX proof, if present
    pre_context: Optional[str]  # text before the statement (regex method + context only)
    post_context: Optional[str] # text after the statement (regex method + context only)

ParseResult

@dataclass
class ParseResult:
    statements: Optional[List[Statement]]
    preamble: Optional[str]
    bibliography: Optional[Dict[str, Dict[str, str]]]
    bibliography_bibtex: Optional[bool]
    method_used: Optional[str] # which method produced `statements` (e.g. "tex", "regex")

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Development

pip install -e ".[dev]"
pytest

The tests/ folder contains small, self-contained LaTeX projects (in tests/fixtures/) that double as readable examples of what each method handles — shared counters, unnumbered environments, thmtools, multi-file \inputs, proof-by-reference, nested macros, and a no-statements paper. The tex tests are skipped automatically when no TeX engine is on PATH, and the llm tests use a mocked backend (no API key needed). See tests/README.md for details.