academic-refchecker 3.0.119

A comprehensive tool for validating reference accuracy in academic papers

RefChecker

Validate reference accuracy in academic papers.
Catch citation errors, fabricated references, and metadata mismatches before they reach reviewers.

Quick Start • Features • Web UI • CLI • Hallucination Detection • Deployment

RefChecker verifies citations against Semantic Scholar, OpenAlex, CrossRef, DBLP, and ACL Anthology, and uses LLM-powered deep web search to flag likely fabricated references. When the LLM finds a more likely source than the first database match, RefChecker re-verifies the citation against the LLM-found metadata before deciding whether it is an error or a hallucination. It supports single papers, bulk batches, and automated scanning of entire OpenReview venues.

Built by Mark Russinovich with AI assistants (Cursor, GitHub Copilot, Claude Code). Watch the deep dive video.

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Contents

• Quick Start
• Features
• Sample Output
• Install
• Web UI
• CLI
• Hallucination Detection
• Bulk Checking
• OpenReview Integration
• Output & Reports
• Deployment
  • Docker
  • Multi-User Server (OAuth)
  • Deploy to Render
• Configuration
• Local Database
• Testing
• License

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Quick Start

Web UI (Docker)

docker run -p 8000:8000 ghcr.io/markrussinovich/refchecker:latest

Open http://localhost:8000 in your browser.

Web UI (pip)

pip install academic-refchecker[llm,webui]
refchecker-webui

CLI (pip)

pip install academic-refchecker[llm]
academic-refchecker --paper 1706.03762
academic-refchecker --paper /path/to/paper.pdf

LLM extraction is generally more accurate, but PDFs can fall back to GROBID when no extraction LLM is configured. Deep hallucination checks require a hallucination-capable LLM provider: OpenAI, Anthropic, Google, or Azure.

Tip: Set SEMANTIC_SCHOLAR_API_KEY for 1-2s per reference vs 5-10s without.

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Features

Category	What it does
Input formats	ArXiv IDs/URLs, PDFs, LaTeX (.tex), BibTeX (.bib/.bbl), plain text
Verification sources	Semantic Scholar, OpenAlex, CrossRef, DBLP, ACL Anthology
LLM extraction	OpenAI, Anthropic, Google, Azure, or local vLLM for parsing complex bibliographies
Metadata checks	Titles, authors, years, venues, DOIs, ArXiv IDs, URLs
Smart matching	Handles formatting variations (BERT vs B-ERT, pre-trained vs pretrained)
Hallucination detection	Flags likely fabricated references using deterministic pre-filters, LLM deep web search, and metadata reverification when the LLM finds a better match
Bulk checking	Upload multiple files or a ZIP in the Web UI; use --paper-list or --openreview in the CLI
OpenReview scanning	Fetch all accepted (or submitted) papers for a venue and scan them in one command
Reports	JSON, JSONL, CSV, or text — with error details, corrections, and hallucination assessments
Corrections	Auto-generates corrected BibTeX, plain-text, and bibitem entries for each error
Web UI	Real-time progress, history sidebar, batch tracking, split extraction/hallucination LLM settings, export (Markdown/text/BibTeX), dark mode
Multi-user hosting	OAuth sign-in (Google, GitHub, Microsoft), per-user rate limiting, admin controls

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Sample Output

Web UI

CLI — Single Paper

📄 Processing: Attention Is All You Need
   URL: https://arxiv.org/abs/1706.03762

[1/45] Neural machine translation in linear time
       Nal Kalchbrenner et al. | 2017
       ⚠️  Warning: Year mismatch: cited '2017', actual '2016'

[2/45] Effective approaches to attention-based neural machine translation
       Minh-Thang Luong et al. | 2015
       ❌ Error: First author mismatch: cited 'Minh-Thang Luong', actual 'Thang Luong'

[3/45] Deep Residual Learning for Image Recognition
       Kaiming He et al. | 2016 | https://doi.org/10.1109/CVPR.2016.91
       ❌ Error: DOI mismatch: cited '10.1109/CVPR.2016.91', actual '10.1109/CVPR.2016.90'

============================================================
📋 SUMMARY
📚 Total references processed: 68
❌ Total errors: 55  ⚠️ Total warnings: 16  ❓ Unverified: 15

CLI — Hallucination Flagging

[5/7] Efficient Neural Network Pruning Using Iterative Sparse Retraining
      Shuang Li, Yifan Chen | 2019
      ❓ Could not verify
      🚩 Hallucination assessment: LIKELY
         A web search for the exact title and authors yields no results in any
         academic database. The paper does not appear in ICML 2019 proceedings,
         indicating it is probably fabricated.

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Install

PyPI (recommended)

pip install academic-refchecker[llm,webui]  # Web UI + CLI + LLM providers
pip install academic-refchecker[llm]        # CLI + LLM providers; recommended for best extraction and hallucination checks
pip install academic-refchecker             # CLI only; PDFs can still fall back to GROBID when available

From Source (development)

git clone https://github.com/markrussinovich/refchecker.git && cd refchecker
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e ".[llm,webui]"
pip install -r requirements-dev.txt                  # pytest, playwright, etc.

Requirements: Python 3.11+. Node.js 20.19+ is only needed for Web UI frontend development.

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Web UI

The Web UI provides real-time progress, check history, batch tracking, and one-click export of corrections.

LLM extraction is preferred, but PDF uploads and direct PDF URLs can fall back to GROBID. Hallucination checks use a separate hallucination LLM selection when one is configured; otherwise the UI falls back to the selected extraction LLM only if that provider supports web search. Local vLLM can be used for extraction, but hallucination checks require OpenAI, Anthropic, Google, or Azure.

refchecker-webui                    # default: http://localhost:8000
refchecker-webui --port 9000        # custom port

Key features:

• Single check — paste an ArXiv URL/ID or upload a PDF/BibTeX/LaTeX file
• Bulk check — upload multiple files (up to 50) or a single ZIP archive; papers are grouped into a batch with a progress bar
• Bulk URL list — paste up to 50 URLs or ArXiv IDs (one per line) to check in a single batch
• Status dashboard — filterable badge counts for errors, warnings, unverified, and hallucinated references
• Reference cards — per-reference details with corrections, source links (Semantic Scholar, ArXiv, DOI), and hallucination assessment
• Export — download corrections as Markdown, plain text, or BibTeX
• History sidebar — browse and re-run previous checks; batches are grouped together
• Settings — separate extraction and hallucination LLM provider/model selection, API key management, Semantic Scholar key validation, local database directory, dark/light/system theme

Frontend Development

cd web-ui && npm install && npm start     # http://localhost:5173

Or run backend and frontend separately:

# Terminal 1 — Backend
python -m uvicorn backend.main:app --reload --port 8000

# Terminal 2 — Frontend
cd web-ui && npm run dev

See web-ui/README.md for more.

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CLI

# ArXiv (ID or URL)
academic-refchecker --paper 1706.03762
academic-refchecker --paper https://arxiv.org/abs/1706.03762

# Local files (PDF, LaTeX, text, BibTeX)
academic-refchecker --paper paper.pdf
academic-refchecker --paper paper.tex
academic-refchecker --paper refs.bib

# With LLM extraction (recommended for complex bibliographies)
academic-refchecker --paper paper.pdf --llm-provider anthropic

# Save human-readable output
academic-refchecker --paper 1706.03762 --output-file errors.txt

# Save structured report (JSON, JSONL, CSV, or text)
academic-refchecker --paper 1706.03762 --report-file report.json --report-format json

# Bulk: check a list of papers
academic-refchecker --paper-list papers.txt --report-file report.json

# OpenReview: fetch and scan an entire venue
academic-refchecker --openreview iclr2024 --report-file report.json

# OpenReview: fetch the paper list only and save it to a custom path
academic-refchecker --openreview aistats2025 --openreview-list-only --openreview-output-file paper_lists/aistats2025.txt

All CLI Options

Input (choose one):
  --paper PAPER              ArXiv ID, URL, PDF, LaTeX, text, or BibTeX file
  --paper-list PATH          Newline-delimited file of paper specs (URLs, IDs, paths)
  --openreview VENUE         Fetch papers from a supported OpenReview venue (iclr, icml, aistats, uai, corl)
  --openreview-status MODE   accepted (default) or submitted
  --openreview-list-only     Fetch the OpenReview paper list and exit without scanning
  --openreview-output-file PATH
                            Custom path for the generated OpenReview paper list

LLM:
  --llm-provider PROVIDER    openai, anthropic, google, azure, or vllm
  --llm-model MODEL          Override the default model for the provider
  --llm-endpoint URL         Custom endpoint (e.g. local vLLM server)
  --llm-parallel-chunks      Enable parallel LLM chunk processing (default)
  --llm-no-parallel-chunks   Disable parallel LLM chunk processing
  --llm-max-chunk-workers N  Max workers for parallel LLM chunks (default: 4)
  --hallucination-provider PROVIDER
                            Separate provider for deep hallucination checks: openai, anthropic, google, or azure
  --hallucination-model MODEL
                            Override the hallucination-check model for the provider
  --hallucination-endpoint URL
                            Custom endpoint for the hallucination-check provider

Verification:
  --database-dir PATH        Directory containing local DBs: semantic_scholar.db, openalex.db, crossref.db, dblp.db, acl_anthology.db
  --s2-db PATH               Path to local Semantic Scholar database
  --openalex-db PATH         Path to local OpenAlex database
  --crossref-db PATH         Path to local CrossRef database
  --dblp-db PATH             Path to local DBLP database
  --acl-db PATH              Path to local ACL Anthology database
  --update-databases         Install/update configured local databases
  --openalex-since DATE      Only ingest OpenAlex partitions newer than YYYY-MM-DD during updates
  --openalex-min-year YEAR   Only ingest OpenAlex works published in YEAR or later during updates
  --db-path PATH             (Deprecated) alias for --s2-db
  --semantic-scholar-api-key KEY   Override SEMANTIC_SCHOLAR_API_KEY env var
  --disable-parallel         Run verification sequentially
  --max-workers N            Max parallel verification threads (default: 6)

Output:
  --output-file [PATH]       Human-readable output (default: reference_errors.txt)
  --report-file PATH         Structured report (includes hallucination assessments)
  --report-format FORMAT     json (default), jsonl, csv, or text
  --debug                    Verbose logging

---

Hallucination Detection

RefChecker automatically evaluates suspicious references for potential fabrication using deterministic filters, LLM deep web search, and metadata reverification.

Stage 1 — Deterministic Pre-filter (no LLM needed)

References are flagged for deeper inspection when they exhibit:

• Unverified status — not found in Semantic Scholar, OpenAlex, CrossRef, DBLP, or ACL Anthology
• Author overlap below 60% — fewer than 60% of cited authors match any known paper (applies to references with 3+ authors)
• Identifier conflicts — DOI or ArXiv ID resolves to a different paper
• URL verification failure — cited URL is broken or points to a different paper

References with only minor issues (year off by one, venue variation) are not flagged.

Stage 2 — LLM Deep Web Search

Flagged references are sent to the configured hallucination LLM for a mandatory web search. The LLM must look for a dedicated page for the cited work, not just a citation in another paper's reference list. It returns a short verdict plus the best link it found and any found title, authors, and year.

Supported hallucination-check providers are OpenAI, Anthropic, Google, and Azure. The CLI can use the extraction provider when it is hallucination-capable, or you can pass --hallucination-provider / --hallucination-model to use a different model. The Web UI exposes the same split as separate extraction and hallucination selectors in Settings.

Stage 3 — Reverification Against LLM-Found Metadata

When the LLM says the reference is probably real (UNLIKELY) and provides found metadata, RefChecker re-runs its normal title, author, and year comparison against that LLM-found metadata. This catches cases where a database lookup matched the wrong edition, version, or similarly titled work. If the cited title/authors/year match the LLM-found source, stale unverified or wrong-match errors can be cleared and the LLM-found URL is added as an llm_verified source. If substantive mismatches remain, the reference stays an error rather than being blindly upgraded.

If the LLM cannot find an exact source, or finds only a similar paper with different authors or identifiers, the reference remains suspicious and can be marked as a likely hallucination.

Each reference receives a verdict:

Verdict	Meaning
🚩 LIKELY	Probably fabricated — no exact source was found, or the found source conflicts substantially with the citation
❓ UNCERTAIN	Inconclusive — may exist but could not be confirmed
✅ UNLIKELY	Probably real — found on a dedicated page with matching title/authors, then rechecked against the cited metadata

Hallucination assessments appear inline in CLI output, in Web UI reference cards, and in structured reports (JSON/JSONL/CSV) via the hallucination_assessment field.

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Bulk Checking

Web UI

Upload multiple files or a ZIP archive to check up to 50 papers in a single batch. Alternatively, paste a list of URLs or ArXiv IDs (one per line). Batches track progress per paper and appear as a group in the history sidebar.

Supported file types: PDF, TXT, TEX, BIB, BBL, ZIP.

CLI

Create a text file with one paper per line (ArXiv IDs, URLs, or local file paths):

1706.03762
https://openreview.net/pdf?id=ZG3RaNIsO8
paper/local_sample.bib
/path/to/paper.pdf

Then run:

academic-refchecker --paper-list papers.txt --report-file bulk_report.json

The report includes per-paper rollups and a cross-paper summary with flagged reference counts.

---

OpenReview Integration

Scan all accepted (or submitted) papers for an OpenReview venue in one command:

# Scan accepted papers
academic-refchecker --openreview iclr2024 --report-file report.json

# Scan all public submissions instead
academic-refchecker --openreview iclr2024 --openreview-status submitted --report-file report.json

Supported venues: ICLR, ICML, AISTATS, UAI, and CoRL.

Use shorthands like iclr2024, icml2025, aistats2025, uai2025, or corl2025.

The command fetches the paper list from OpenReview, writes it to output/openreview_<venue>_<status>.txt by default, and then runs a bulk scan. Use --openreview-list-only to generate the list without running verification, and --openreview-output-file to choose the output path. The structured report includes per-paper rollups with flagged record counts and error-type distributions, making it easy to triage an entire conference for citation problems.

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Output & Reports

Result Types

Type	Description	Examples
❌ Error	Critical issues needing correction	Author/title/DOI mismatches, incorrect ArXiv IDs
⚠️ Warning	Minor issues to review	Year differences, venue variations
ℹ️ Suggestion	Recommended improvements	Add missing ArXiv/DOI URLs
❓ Unverified	Could not verify against any source	Rare publications, preprints
🚩 Hallucination	Likely fabricated reference	Unverifiable with rich metadata, identifier conflicts

Structured Reports

Write machine-readable reports with --report-file and --report-format:

academic-refchecker --paper 1706.03762 --report-file report.json --report-format json

Example JSON report structure

{
  "generated_at": "2026-03-15T19:50:52Z",
  "summary": {
    "total_papers_processed": 1,
    "total_references_processed": 7,
    "total_errors_found": 2,
    "total_warnings_found": 2,
    "total_unverified_refs": 4,
    "flagged_records": 3,
    "flagged_papers": 1
  },
  "papers": [
    {
      "source_paper_id": "local_hallucination_7ref_sample",
      "source_title": "Hallucination 7Ref Sample",
      "total_records": 6,
      "flagged_records": 3,
      "max_flag_level": "high",
      "error_type_counts": { "unverified": 3, "multiple": 2, "year (v1 vs v2 update)": 1 },
      "reason_counts": { "unverified": 3, "web_search_not_found": 3 }
    }
  ],
  "records": [
    {
      "ref_title": "Deep Residual Learning for Image Recognition",
      "ref_authors_cited": "Jian He, Xiangyu Zhang, Shaoqing Ren, Jian Sun",
      "ref_authors_correct": "Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun",
      "error_type": "multiple",
      "error_details": "- First author mismatch ...\n- Year mismatch ...",
      "ref_corrected_bibtex": "@inproceedings{he2016resnet, ... year = {2015} ...}",
      "hallucination_assessment": { "verdict": "UNLIKELY", "explanation": "..." }
    }
  ]
}

CLI output examples

❌ Error: First author mismatch: cited 'Jian He', actual 'Kaiming He'
❌ Error: DOI mismatch: cited '10.5555/3295222.3295349', actual '10.48550/arXiv.1706.03762'
⚠️ Warning: Year mismatch: cited '2019', actual '2018'
ℹ️ Suggestion: Add ArXiv URL https://arxiv.org/abs/1706.03762
❓ Could not verify: Llama guard (M. A. Research, 2024)
🚩 Hallucination assessment: LIKELY — no matching paper found in academic databases

Each report record includes the original reference, error details, corrected metadata (BibTeX, plain text, bibitem), verified URLs, and hallucination assessment when applicable.

---

Deployment

Docker

Pre-built multi-architecture images are published to GitHub Container Registry on every release.

# Quick start
docker run -p 8000:8000 ghcr.io/markrussinovich/refchecker:latest

# With LLM API key (recommended)
docker run -p 8000:8000 -e ANTHROPIC_API_KEY=your_key ghcr.io/markrussinovich/refchecker:latest

# Persistent data
docker run -p 8000:8000 \
  -e ANTHROPIC_API_KEY=your_key \
  -v refchecker-data:/app/data \
  ghcr.io/markrussinovich/refchecker:latest

Other LLM providers:

docker run -p 8000:8000 -e OPENAI_API_KEY=your_key ghcr.io/markrussinovich/refchecker:latest
docker run -p 8000:8000 -e GOOGLE_API_KEY=your_key ghcr.io/markrussinovich/refchecker:latest

Docker Compose

git clone https://github.com/markrussinovich/refchecker.git && cd refchecker
cp .env.example .env   # Add your API keys
docker compose up -d

docker compose logs -f    # View logs
docker compose down       # Stop
docker compose pull       # Update to latest

Tag	Description	Arch	Size
latest	Latest stable release	amd64, arm64	~800MB
X.Y.Z	Specific version (e.g., 2.0.18)	amd64, arm64	~800MB

Multi-User Server (OAuth)

By default, RefChecker runs in single-user mode — no login required. Enable multi-user mode for shared deployments where each visitor signs in via OAuth. LLM API keys are entered per-user in the Settings panel, stored in the browser's localStorage, and sent per-request — never stored on the server.

1. Generate a JWT Secret Key

python -c "import secrets; print(secrets.token_hex(32))"

2. Register an OAuth Application

Configure at least one provider:

Provider	Registration URL	Callback URL
Google	Google Cloud Console	https://<domain>/api/auth/callback/google
GitHub	GitHub Developer Settings	https://<domain>/api/auth/callback/github
Microsoft	Azure App Registrations	https://<domain>/api/auth/callback/microsoft

3. Configure Environment Variables

cp .env.example .env

REFCHECKER_MULTIUSER=true
JWT_SECRET_KEY=<output from step 1>
SITE_URL=https://<your-domain>
HTTPS_ONLY=true

# At least one OAuth provider
GOOGLE_CLIENT_ID=...
GOOGLE_CLIENT_SECRET=...

GITHUB_CLIENT_ID=...
GITHUB_CLIENT_SECRET=...

MS_CLIENT_ID=...
MS_CLIENT_SECRET=...

# Optional
REFCHECKER_ADMINS=github:you  # comma-separated; first sign-in is auto-admin
MAX_CHECKS_PER_USER=3         # max concurrent checks per user (default: 3)

4. Launch

docker compose up -d

Or without Docker:

pip install "academic-refchecker[llm,webui]"
REFCHECKER_MULTIUSER=true JWT_SECRET_KEY=<secret> GOOGLE_CLIENT_ID=... GOOGLE_CLIENT_SECRET=... \
  refchecker-webui --port 8000

Verify:

curl http://localhost:8000/api/auth/providers
# {"providers":["google","github"]}

Notes:

• The first user to sign in is automatically admin. Add more via REFCHECKER_ADMINS.
• Each user may run up to MAX_CHECKS_PER_USER concurrent checks (default 3). The 4th returns HTTP 429.
• The CLI is unaffected — academic-refchecker works without any auth configuration.
• Place the server behind a TLS-terminating reverse proxy (nginx, Caddy) for HTTPS.

Deploy to Render

RefChecker includes a render.yaml Blueprint for one-click deployment to Render:

1. Fork this repo (or connect your own copy).
2. On Render, click New + → Blueprint → select the repo.
3. Render reads render.yaml and creates the service with a persistent disk.
4. Set environment variables in the Render dashboard (Environment tab):
   • SITE_URL — your public URL including https:// (must match exactly — OAuth fails otherwise).
   • HTTPS_ONLY=true for production.
   • REFCHECKER_DATA_DIR=/data (matches the persistent disk mount).
   • At least one OAuth provider's CLIENT_ID / CLIENT_SECRET.
5. Register each provider's callback URL as https://<your-url>/api/auth/callback/{google,github,microsoft}.

Note: The persistent disk at /data stores the SQLite database and uploaded files, so data survives redeployments. For other PaaS hosts (Railway, Fly.io), the same Docker image works — set PORT, REFCHECKER_DATA_DIR, and the auth env vars.

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Configuration

LLM Providers

LLM-powered extraction improves accuracy with complex bibliographies. Hallucination detection is configured separately so you can use one model for extraction and another, web-search-capable model for deep hallucination checks. Claude Sonnet 4 performs best for extraction; GPT-4o may hallucinate DOIs.

Provider	Env Variable	Example Model
Anthropic	ANTHROPIC_API_KEY	claude-sonnet-4-6
OpenAI	OPENAI_API_KEY	gpt-4.1
Google	GOOGLE_API_KEY	gemini-2.5-flash
Azure	AZURE_OPENAI_API_KEY	gpt-4.1
vLLM	(local)	meta-llama/Llama-3.3-70B-Instruct

export ANTHROPIC_API_KEY=your_key
academic-refchecker --paper 1706.03762 --llm-provider anthropic

academic-refchecker --paper paper.pdf --llm-provider openai --llm-model gpt-4.1
academic-refchecker --paper paper.pdf --llm-provider vllm --llm-model meta-llama/Llama-3.3-70B-Instruct

# Use one model for extraction and another for hallucination checks
academic-refchecker --paper paper.pdf \
  --llm-provider vllm --llm-model meta-llama/Llama-3.3-70B-Instruct \
  --hallucination-provider anthropic --hallucination-model claude-sonnet-4-6

Hallucination-capable providers are OpenAI, Anthropic, Google, and Azure. vLLM can extract references but cannot perform live web search, so pair it with --hallucination-provider when you want hallucination checks.

Local Models (vLLM)

Run an OpenAI-compatible vLLM server for local inference:

pip install "academic-refchecker[vllm]"
python scripts/start_vllm_server.py --model meta-llama/Llama-3.3-70B-Instruct --port 8001
academic-refchecker --paper paper.pdf --llm-provider vllm --llm-endpoint http://localhost:8001/v1

Environment Variables

# LLM
export REFCHECKER_LLM_PROVIDER=anthropic
export ANTHROPIC_API_KEY=your_key           # Also: OPENAI_API_KEY, GOOGLE_API_KEY

# Performance
export SEMANTIC_SCHOLAR_API_KEY=your_key    # Higher rate limits / faster verification

---

Local Database

For offline verification or faster processing:

python scripts/download_db.py \
  --field "computer science" \
  --start-year 2020 --end-year 2024

academic-refchecker --paper paper.pdf --s2-db semantic_scholar_db/semantic_scholar.db
academic-refchecker --paper paper.pdf --database-dir /path/to/local-db-folder
academic-refchecker --database-dir /path/to/local-db-folder --update-databases
academic-refchecker --database-dir /path/to/local-db-folder --update-databases --openalex-min-year 2020

--update-databases now refreshes local S2, DBLP, and OpenAlex databases when those paths are configured. DBLP follows Hallucinator's offline-dump approach by downloading and parsing dblp.xml.gz, while OpenAlex follows Hallucinator's S3 snapshot model and can be scoped with --openalex-since or --openalex-min-year to avoid a full build. CrossRef remains API-first; RefChecker will still use live CrossRef lookups, but offline CrossRef population is not automated yet.

When the Web UI has local databases configured, it scans REFCHECKER_DATABASE_DIRECTORY for well-formed DB names (semantic_scholar.db, openalex.db, crossref.db, dblp.db) and schedules asynchronous background refresh tasks for discovered DBs. Background refresh uses the bundled local database updater for discovered S2, DBLP, and OpenAlex files. The downloader also writes a latest_snapshot.txt file next to the SQLite database for operator visibility, while the Web UI shows the current snapshot from the database metadata in the settings panel.

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Documentation

Detailed project documentation lives under docs/README.md, including the Web UI guide and testing guide.

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Testing

680+ tests covering unit, integration, and end-to-end scenarios.

pytest tests/                    # All tests
pytest tests/unit/              # Unit only
pytest tests/e2e/               # End-to-end (Playwright)
pytest --cov=src tests/         # With coverage
make clean                      # Remove generated local artifacts (logs, debug output, cache, build files)

See tests/README.md for details.

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License

MIT License — see LICENSE.