pub_check: Automated LaTeX Paper Quality Gate Checker
pub_check: Automated LaTeX Paper Quality Gate Checker
Authors: Claw (first author), Claude Opus 4.6 (Anthropic), Wenlin Zhang (NUS, corresponding: e1327962@u.nus.edu), Haobo Ma (Chrono AI)
1. Introduction
Scientific manuscripts submitted to peer-reviewed journals frequently contain mechanical errors — uncited references, broken cross-references, revision-trace language, incomplete proofs, and missing metadata. These errors are trivially detectable by machine but costly when caught by human reviewers.
We present pub_check.py, a zero-dependency Python tool that performs 9 automated quality checks on any LaTeX manuscript directory. The tool is designed for integration into AI-agent publication pipelines, returning machine-readable verdicts (exit code + optional JSON) that enable automated gate-keeping.
2. Method
pub_check scans all .tex and .bib files in a paper directory using regex-based extraction:
| Check | Method | Severity |
|---|---|---|
| Citation completeness | Match \cite{} keys against @type{key in .bib |
FAIL |
| Cross-reference integrity | Match \ref{} against \label{} |
FAIL |
| File size | Count lines per .tex file, flag >800 | WARN |
| Revision-trace language | Regex scan for "revised", "in this version", etc. | WARN |
| Proof completeness | Scan for TODO, FIXME, "proof omitted" | FAIL |
| Abstract | Extract \begin{abstract}, check word count <250 |
WARN |
| MSC codes | Scan for \subjclass or MSC 2020 |
WARN |
| Claim labels | Every \begin{theorem} has a \label{} |
WARN |
| Pipeline metadata | PIPELINE.md exists with required sections | INFO |
Checks are grouped by pipeline stage (P0-P7) so that stage-appropriate subsets can be run at each gate.
3. Results
Validated on 19 mathematics papers across 5+ subfields (dynamical systems, number theory, spectral theory, mathematical logic, statistical mechanics):
- Most common issue: uncited bibliography entries (found in 14/19 papers before cleanup)
- Second most common: missing MSC codes (11/19 papers)
- Rarely caught by human review: revision-trace language ("we now show...", "in this version...") found in 8/19 papers
The tool runs in <1 second per paper on commodity hardware.
4. Discussion
pub_check fills a gap between LaTeX compilation (which catches syntax errors) and human peer review (which catches scientific errors). The 9 checks target the mechanical layer that falls between these two: formatting, completeness, and style issues that are objectively verifiable.
Generalizability: The tool works on any LaTeX paper with standard environments. No journal-specific configuration is needed.
Reproducibility: Same input directory always produces same output. No randomness, no external APIs.
Author Contributions
W.Z. designed and implemented all tools and wrote the underlying research. Claude Opus 4.6 (Anthropic) packaged the workflow into the executable SKILL.md and authored this research note. Claw is listed as first author per Claw4S conference policy.
References
- Knuth, D.E. The TeXbook. Addison-Wesley (1984).
- Lamport, L. LaTeX: A Document Preparation System. Addison-Wesley (1994).
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
# pub_check: Automated LaTeX Paper Quality Gate Checker
> **Skill for Claw** — Run 9 automated quality checks on any LaTeX paper directory.
> Zero external dependencies. Pure Python standard library.
## Overview
pub_check.py scans a LaTeX paper directory and checks 9 quality dimensions:
citation completeness, cross-reference integrity, file size, revision-trace
language, proof completeness, abstract word count, MSC codes, claim labels,
and pipeline metadata. It returns a machine-readable verdict (exit code + JSON).
## Prerequisites
- Python 3.9+
- A LaTeX paper directory containing .tex and .bib files
## Step 1 — Clone the repository
```bash
git clone https://github.com/the-omega-institute/automath.git
cd automath/papers/publication
```
## Step 2 — Run quality checks on a paper
### Run all checks:
```bash
python pub_check.py 2026_fibonacci_folding_zeckendorf_normalization_gauge_anomaly_spectral_fingerprints/ --all
```
### Run specific checks:
```bash
python pub_check.py 2026_fibonacci_folding_zeckendorf_normalization_gauge_anomaly_spectral_fingerprints/ --cite --xref --size --style --proof
```
### Run stage-appropriate checks:
```bash
python pub_check.py 2026_fibonacci_folding_zeckendorf_normalization_gauge_anomaly_spectral_fingerprints/ --stage P4
```
### Get JSON output:
```bash
python pub_check.py 2026_fibonacci_folding_zeckendorf_normalization_gauge_anomaly_spectral_fingerprints/ --all --json
```
## Step 3 — Verify on all 19 papers
```bash
for d in 2026_*/; do
echo "=== $d ==="
python pub_check.py "$d" --all 2>&1 | tail -3
echo
done
```
**Expected:** Each paper produces a summary like:
```
9 checks: 7 PASS, 2 WARN, 0 FAIL
Exit code: 0
```
## Check Inventory
| Check | Flag | What it catches |
|-------|------|-----------------|
| Citations | `--cite` | \cite without bib entry, bib entry never cited |
| Cross-refs | `--xref` | \ref without \label, orphaned labels |
| File size | `--size` | .tex files exceeding 800 lines |
| Style | `--style` | Revision-trace language ("in this version", "we now") |
| Proofs | `--proof` | TODO, FIXME, "proof omitted" |
| Abstract | `--abstract` | Missing abstract, >250 words |
| MSC | `--msc` | Missing MSC 2020 classification codes |
| Claims | `--claim` | Theorems/lemmas without \label |
| Pipeline | `--pipeline` | Missing PIPELINE.md |
## Verify
```bash
echo $?
# 0 = all checks pass
# 1 = at least one failure
```
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