reference-audit

Audit the references in a paper. Given a .bib bibliography and the .tex that cites it — or just the paper's PDF — reference-audit figures out which real-world document each reference actually points to and flags ones that don't match anything real (hallucinated references).

For every entry it returns one of four verdicts:

• exactly one match — the reference resolves to a single real document (with its DOI/ISBN/URL, and any preprint↔published versions merged).
• no match — no real document corresponds to the entry (a likely hallucination).
• multiple matches — several accepted candidate groups remain after same-work clustering.
• contradiction — a hard paper identifier resolves to a real paper, but supplied citation fields substantively disagree with that paper. The report preserves the ID target and conflict evidence.

It also backfills missing DOIs/ISBNs, normalizes malformed identifiers, and reports dangling or uncited citations.

Designed for three audiences: authors polishing their bibliography, automated screening of submissions for hallucinated references, and AI agents writing papers. The full product specification lives in architecture/SPEC.md.

Quick Start

You need uv. Install the tool from PyPI (uv fetches Python 3.14 if your system lacks it):

uv tool install reference-audit

Then audit a bibliography:

reference-audit audit references.bib

Pass the manuscript before the .bib (reference-audit audit paper.tex references.bib) to also find entries that are never cited and \cite keys with no entry. With only a PDF, see Auditing a PDF.

No accounts or keys are needed for a first run. The report opens with the two lists that matter most:

• CAPITAL OFFENCES: entries that match no real document, which makes them likely hallucinations.
• UNABLE TO VERIFY: entries the tool could not check, each with the reason (a source that failed, an ambiguity it could not settle, a dead link). These are not hallucinations. They are references a person still has to check.

Below them, ISSUES and FORMATTING NITS & ADVISORIES list wrong or missing fields, DOIs, and better versions to cite. Reading the output explains every line. Add --format json for a machine-readable report.

Results are cached in .reference_audit/cache.db beside the .bib, so a re-run after you fix some entries only re-checks what changed and what failed last time.

Settings and API keys go in ~/.config/reference-audit/config.toml. Keep the file private (chmod 600). The two sections below describe the settings worth adding. Configuration lists every setting and the other places they can be set.

Adding API keys for the data sources

Without keys, the scholarly databases serve the tool from their shared public allowances. On a large bibliography, or when you run audits often, some lookups then fail with rate-limit or quota errors. Those entries end up under UNABLE TO VERIFY naming the source and its error, and retries with backoff slow the run down. Registering with the sources gives you your own allowance:

crossref_mailto = "you@example.org"      # Crossref and OpenAlex "polite pool" contact
openlibrary_email = "you@example.org"    # identifies you to Open Library
s2_api_key = "..."                       # Semantic Scholar: https://www.semanticscholar.org/product/api
openalex_api_key = "..."                 # OpenAlex: https://docs.openalex.org
google_books_api_key = "..."             # Google Books: https://developers.google.com/books/docs/v1/using#APIKey

Add only the ones you need. Failures from earlier runs are never cached, so the next run retries them. An invalid key makes every query to that source fail, and each affected entry says so; it is never read as "not found". For very large batches, Semantic Scholar, OpenAlex and DBLP can also be read from a local ClickHouse mirror, which has no rate limits.

Adding an LLM API key

The tool decides most entries by deterministic rules. Some it cannot settle that way: a candidate that is close to the entry but not an exact match, two records that may or may not be the same work, a field difference no rule covers. With an LLM, each of these is judged one candidate at a time. Without one, those entries are reported under UNABLE TO VERIFY with the reason … need LLM adjudication, but the LLM is not configured. Nothing is guessed either way.

To turn the LLM on, add an OpenAI key, or the key and URL of any OpenAI-compatible endpoint:

openai_api_key = "sk-..."
# openai_base_url = "https://..."        # an OpenAI-compatible endpoint
# model = "..."                          # default: gpt-6-luna; or pass --model per run

The LLM also enables --check-citations, which checks each citing sentence against the cited work's abstract. LLM answers are cached like everything else. Use --no-llm for a deterministic run even when a key is configured.

Documentation

• How it works: the identification funnel, the sources, books, web pages, and caching.
• Usage: command-line options, reading the report, and citation alignment (--check-citations).
• Installation and configuration: requirements, installation, and every setting.
• Auditing a PDF: GROBID extraction, and PDFs that embed their references.
• Local ClickHouse backend: reading Semantic Scholar, OpenAlex and DBLP from a local mirror.
• Development: tests, the PDF extraction oracle, benchmarking, and the project layout.
• HALLMARK benchmark: verdict accuracy on a citation-hallucination benchmark.
• Constructor Fabric: how the project is specified, traced, and validated with cfs.
• Product specification and the governed design artifacts in architecture/.