academic-refchecker 3.0.150

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

 

_{Linux: .AppImage · .deb · all builds}

_{Native desktop builds powered by Tauri · Built and signed by GitHub Actions on every release tag.}

✨ What the desktop app adds

The desktop app wraps the same engine in a native Tauri shell and layers on a full review workspace. The highlights below are grouped and collapsed — click any section to expand it.

Newest in v0.9.x — a 2×2 article-tools grid (Retractions · Gap-finder · Citation-numbering · Chat) with full-width detail panels · Chat-with-PDF & Summarize, grounded in the article text · Similar papers + "Cites & Refs" with a ResearchRabbit-style Explore graph · Teams & realtime shared-batch presence · journal-name & author hovers (h-index · ORCID · author guidelines) · an inline-citation numbering checker · AI-generated-text detection with rich visualizations · and a fully legible, colour-gradient CLI banner.

🤖 AI-generated-text detection — opt-in, advisory, never proof of misconduct

• Three engines, your choice. A local calibrated model (desklib DeBERTa-v3, MIT — runs offline after a one-time download), an LLM-judge that reuses your hallucination-check provider, or an external API (Pangram / GPTZero, key + explicit consent required).
• GPTZero-style visualizations. A confidence donut, AI / Mixed / Human probability pills, a page-by-page likelihood breakdown, and Top AI / Top Human sentence lists — all descriptive of the model's windowed scores, never a probability of guilt.
• Flagged passages, in context. Click any advisory passage to see it highlighted in the document, with zoom + find.
• Run mode. Settings → Run mode lets you check references only (default), AI text only (skips reference verification entirely), or both.
• Honesty-first. A permanent disclaimer, distinct inconclusive / unavailable states, and abstention on short or highly technical text. The model is cited in-app and in Sources & credits.

📤 Share, export & document viewing

• Share this document. One click produces a self-contained HTML report (references + verdicts + AI-detection visuals, all inline) you can download and send anywhere.
• Publish a link. Opt-in Publish to web pushes the report to a host (GitHub Gist → viewable link) so anyone with the link can view the results.
• Video walkthrough. Export an in-app animated WebM of the verdict gauge, reference stats, and AI band — no external tools, no screen-share.
• Native-feeling document viewer. The extracted body renders as a centered, serif "page" with flagged passages highlighted in place, plus zoom and an in-document find bar (⌘F, match navigation).
• PDF page viewer with zoom. Browse the original PDF pages full-screen with +/- zoom and fit.
• Reference-manager export (RIS). Imports straight into Zotero, EndNote, Mendeley, Rayyan, Papers, and RefWorks — with the verifier's corrected metadata, not the wrong-as-cited values. Includes a Sort control (citation order / alphabetical / year).

🕸️ Graphs & the reference library

• Obsidian-style 3D library graph. Visualize every reference you've ever verified as a 3D force-directed graph — node size = how many times it's been seen, edges = shared authors / venue.
• Real per-paper citation graph. Force-directed view of one paper's bibliography, edges from the real Semantic Scholar citation graph (A → B iff A cites B), nodes sized by in-paper in-degree. Double-click a node to expand one hop further; toggle an AI-likelihood ring on the nodes.
• Global reference library (Seen References). Every verified reference is persisted to a global identity cache (DOI / arXiv / normalized-title key) and consulted automatically for instant matches. Live-refreshing, searchable, and clearable.
• Find similar papers — multi-source, actively verified. Candidates from Semantic Scholar, OpenAlex, your web-search provider, and your default LLM, deduped with source badges and re-verified before display — real ✓ verified / ? unconfirmed, not just metadata.

✏️ Corrections, citation styles & live health

• Add / Remove / Suggest alternative — everywhere. In both the References and Corrections tabs. Apply Fix merges the verifier's suggested metadata and re-verifies live, so the ref flips to verified and the health chip moves in real time (apply-all parallelizes 4 in flight).
• Suggest alternative combines an LLM "what real paper did they mean?" with Semantic Scholar title-search — each candidate rendered in your selected citation style with one-click Copy.
• Live citation-health chip. A minimal Grammarly-style score in the Summary header — color-coded, hover for a breakdown, recomputes on every edit, copyable as a Markdown badge.
• Tunable citation styles + custom-style builder. APA / IEEE / Vancouver / etc. expose Max-authors, et-al threshold, and Include-URL toggles; save a custom template like {authors} ({year}). {title}. {venue}. {doi}.
• Author cards on hover. Hovering an author shows affiliation, paper & citation counts, h-index, homepage, and recent papers (Semantic Scholar, cached). An inline-cited ✓ badge marks references actually cited in the body text.

⚙️ Extraction, cost tracking & quality-of-life

• Cascade extraction (token saver). Reference Extraction picks cascade (regex / BibTeX / GROBID first, LLM only on messy entries) or LLM-only — typically 60–90% fewer LLM tokens on well-formatted papers.
• LLM token + cost meter. Tracks tokens and an estimated USD cost across every provider, with per-provider and per-kind breakdowns and a cascade-savings hint. Persists across restarts.
• Citation context. Each card shows the sentence where the reference is cited — numeric [12] and author-year (Smith et al., 2020) styles, with a retry/fallback that also catches narrative & title-mention citations.
• Batch workspace. Run hundreds of papers, with a batch summary view, per-paper status, expand/collapse-all, and aggregate counters.
• Drag-and-drop + Open With. Drop a PDF / DOCX / ODT / RTF / Markdown / HTML / BibTeX / LaTeX / text file — or right-click → RefChecker in Finder/Explorer — and the check starts immediately.
• Auto-updating, signed builds. Native installers for macOS / Windows / Linux, signed and shipped by GitHub Actions on every release tag, with a built-in updater.

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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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🆕 Recent updates

Latest release highlights (desktop app)

• v0.9.22 — Article-tools layout + "sometimes missing" fixes. The four on-demand article tools (Retractions · Gap-finder · Citation-numbering · Chat & Summarize) now sit in a tidy 2×2 button grid; clicking one opens its details full-width directly below (one at a time — the buttons never shift). Fix: the native document viewer no longer comes up empty for pasted text / .bib / .bbl sources (the extracted text is now served). Fix: inline "cited in…" contexts no longer silently vanish on a re-check — when references load from cache the manuscript body is re-read so citation contexts (and AI detection) have text to work with. Settings: the Accounts & Teams label is left-aligned and the panel header shows the full name. (The "Citation numbering — n/a · mixed citation styles" message is honest abstention, not a bug: flagging a genuinely mixed-style paper would be a false alarm.)
• v0.9.21 — Progress + chips. Fix: the reference progress can no longer read past 100% (28/23 · 122%, 59/43) — total_refs is reconciled to the real reference count across the backend and the UI, so the bar always lands at 100%. The "Filter by issue" chips are aligned to the button design-system (one 8px radius, colour-only hover/selected, proper toggle semantics). (Release builds are now pinned via a committed Cargo.lock so a drifting Rust dependency can't break a desktop build.)
• v0.9.20 — Reliability + polish. Fix (P0): checks no longer get stuck in_progress forever — a reconciler finalizes orphaned checks (e.g. after a restart or a hung detection) on startup and on demand, and AI detection is now hard-bounded so it always returns. AI-generated-text result no longer gets trapped behind a stuck finalization. The Share card/video counts now match the results bar exactly (errors are no longer double-counted with hallucinations). Author matching handles Brazilian/Iberian names where the cited surname is a middle compound (de Oliveira SD ↔ Danilo de Oliveira Silva). Each reference gets a Funding button (opens the funder/grant data, like Abstract). The walkthrough video shows only in the Share popup (not the stats summary), and the accounts/sign-in text is left-aligned.
• v0.9.19 — Multi-detector AI detection: install and run any mix of the best open-source detectors (desklib · SuperAnnotate · e5-small-LoRA · MAGE; heavy zero-shot ones listed as opt-in), compare their scores side-by-side with per-sentence agreement, and export exactly the detectors you tick — plus CLI --detectors / --list-detectors. In-PDF navigation: clicking an inline citation jumps to the entry inside the PDF, whole-sentence highlights, Cmd/Ctrl+F find in every native viewer, pinch-zoom in the context overlay. Teams: share checks with your team and work the same batch together; enabling accounts applies without a restart. Add-to-list rejects duplicates, commits the renumbered reference list, and renders tracked was→should-be changes into the PDF. Live token + $ meter across every LLM flow; one canonical count across badge / report / export; the share video uses real per-article numbers and the stats page gets its own. Chat and Summarize choose separate models; per-reference chat grounds in the fetched full text when open access allows (honest TL;DR fallback). Plus: AS-CITED corrections carry the verified DOI, suggested papers gain verification + provenance links, "et al." expands to the full author list, ORCID iD + link in the author card, alphabetic numbering schemes, unified buttons with no click-jump, and a full CLI parity / feature-matrix docs pass.
• v0.9.18 — A working email-support link; Retraction / Gap-finder / Citation-numbering are now per-article in a batch (no more cross-leak); the walkthrough is gone from the stats page and the video plays once in the share banner; "Unknown mismatch" fixed (accented venues/authors — "Bengio" vs "Béngio" — are no longer flagged as spurious mismatches); clickable DOIs in the library graph; author hover gains Semantic Scholar / Google Scholar logo links, a per-reference Chat button, and a per-card Remove from the library.
• v0.9.17 — Enable accounts & Teams from inside the app — Settings now has a real form: flip on multi-user mode, paste your Google / GitHub / Microsoft OAuth credentials, and Apply — the app saves them, relaunches, and the sign-in screen + Teams light up. No more hand-editing a .env or restarting the server by hand. Single-user stays the default; secrets are stored locally (write-only, never echoed back).
• v0.9.16 — Discovery modes reworked to References / Citations / Both — find papers that share your references, share your citations (co-cited), or both, on real OpenAlex data. A "Chat about this reference" button (grounded on that reference). The author hover card now shows h-index + citation count. Adding a suggested reference shows a clear before→after renumber diff ([5] → [6], accent-highlighted).
• v0.9.15 — Export reports restyled to match the app (HTML/PDF/Markdown/Word — dark + light, green accent, status colours, Mac-native type) with a governing docs/design.md. Sign-in & Teams surfaced in the app (Settings/header) so accounts + team editing are reachable. Journal-name hover now shows a single popover. The per-check walkthrough is wired into the article stats.
• v0.9.14 — Native "view in document": the flagged-passages / AI-detection viewer now renders the real PDF (pdf.js) with colour-coded status highlights and click-to-jump-to-reference back-links; sources that aren't already a PDF (pasted text, .tex, .bib, .txt) are converted to a self-contained PDF so they render the same way (graceful text fallback). PDF viewer defaults to fit-width (no longer over-zoomed) with trackpad pinch-zoom. Explore graph no longer renders empty/collapsed — it builds reactively with a readable spread layout. Per-AI-sentence "View in document" buttons. Redundant "Published" badge removed; Full link + Funding added. Enrichment now cross-fills from all sources so references show maximum info.
• v0.9.13 — Fix (P0): Re-verify / Suggest-alternative / Remove no longer blank the whole page (a rules-of-hooks crash, React #310) — plus an app-wide error boundary so any render error shows a recoverable screen instead of a blank window. The retraction check button stays clickable (turns green when clean) and re-runs on click; info-only badges (Published, Topics) no longer look clickable; the author hover card scrolls when long; the scroll-to-top button no longer overlaps content. Teams gain an activity log (who created the team, added/removed which member, who left) · the radial library graph shows full article info on hover · calmer 3D graph glow · bug reports open the upstream repo · reliable email support.
• v0.9.12 — Fix: batch checks failed with a 500 ('BatchUrlsRequest' object has no attribute 'semantic_scholar_api_key') — the batch request model now carries the full per-check config (LLM · hallucination · Semantic Scholar · AI-detection · detection-mode), matching single checks. Plus a fully legible, colored CLI banner — the REFCHECKER wordmark renders as renderer-independent pixels (no font dependency, no text overlap), and colour now follows the actual output stream (stderr), so the gradient stays even when stdout is piped (e.g. --report-format json > out.json).
• v0.9.11 — A bolder, more legible CLI banner — the REFCHECKER wordmark now uses solid 2-cell-wide strokes instead of thin scattered blocks.
• v0.9.10 — Fix: the Chat/Summarize abstract-fallback crashed on Python 3.11 (a mid-pattern inline-regex flag — re.error: global flags not at the start); CI is green across the full Python-3.11 suite again.
• v0.9.9 — Hardening: the Published badge now renders the backend's real human date (and a Topics badge from OpenAlex fields-of-study + a live Checking/Pending status pill); Chat & Summarize shows an honest "configure a model in Settings" empty-state; Teams gains remove-member / leave-team and a hardened presence roster. +26 frontend + several backend tests.
• v0.9.8 — Similar → "Cites & Refs" (and Both) mode (real OpenAlex referenced_works + cites:) · Chat-with-PDF + Summarize, grounded in the article text with a per-feature model selector and honest abstain when no text · Teams (create / members) + realtime shared-batch presence · ResearchRabbit-style Explore graph · journal-name hover (OpenAlex /sources metadata + DOAJ author-guidelines link) · author h-index / ORCID backfill for non-Semantic-Scholar authors · inline ordering-consistency check (alphabetical vs order-of-appearance). CI: Tauri-bundle retry to ride out GitHub-CDN 504s.
• v0.9.6 — "Additional Info" bar under each reference (Abstract · Claim/TL;DR · Preprint · open-access Full text · Add to Library) · Support menu in the header (email + open a GitHub issue) · Mac-native system-font design tokens · author hover gains a Google Scholar link and is scrollable · fixes the "Did you miss these" 404 (stale bundled frontend regenerated).
• v0.9.3–v0.9.5 — Fixes: all share/export 500s (live-breaking) · the suggested correction now includes the year/venue the warnings name · "Checking for hallucination" no longer hangs · the two summary badges reconcile. Features: inline-citation numbering checker (gaps / out-of-order / duplicates / undefined / uncited, scheme-aware, adversarially hardened) with a badge · Add to references with a before/after renumber doc-diff preview · per-reference enrichment (abstract · TL;DR · OA-PDF · preprint) · view-in-document opens zoomed + scroll-centered on the cited sentence with a status-coloured pulse and a two-way reference↔document link · radial-graph edge-pinning + an Obsidian-style 3D graph (bloom, click-to-spotlight, flow particles).
• v0.8.1 — Fixes: Share → Download HTML now produces a complete report (was 500 / empty references); AI detection no longer skipped for some papers in bulk; author-matching tolerates surname typos (Guruprasad↔Guruprashad) and a small omission in an otherwise-correct author list; the session token/$ meter no longer resets mid-run. UX: PDF viewer zoom moved to the side + ⌘F / Ctrl+F find; the Share dialog shows an in-page results animation while building the report (the downloadable-video button was removed) and the Share button is now a prominent action.
• v0.8.0 — Modern CLI banner (block-pixel REFCHECKER wordmark, gradient, grouped command / environment / help panels); the AI-detection panel and Top AI / Human sentences are now collapsible; refreshed README with animated SVGs.
• v0.7.99 — Detection run-mode: run references only, AI detection only, or both. In-app citation of the local detection model (desklib, Hugging Face). Animated README banners.
• v0.7.98 — AI-detection visualizations (confidence donut, AI/Mixed/Human pills, page-by-page bands, Top AI/Human sentences) · Share this document (self-contained HTML, publish link, video) · 3D Seen-References library graph · document-viewer zoom + find · richer author hover cards · inline-cited ✓ badge · title-typo & "unknown mismatch" fixes.
• v0.7.96 — Upstream sync with markrussinovich/refchecker · sidebar expand/collapse-all for batches.

See the full release list for every build.

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Contents

• Quick Start
• Features
• Feature Matrix (Web / Desktop / CLI / API)
• Sample Output
• Install
• Web UI
• CLI
• Hallucination Detection
• AI-Generated Text 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
AI-generated-text detection (opt-in)	Optionally analyzes the body text of each checked article for AI-generated-likelihood, returning a low/medium/high band plus advisory flagged passages. Three engines: a local calibrated model (offline, downloadable), an LLM judge (reuses your configured LLM), or an external API (Pangram/GPTZero). Advisory only — detection is unreliable on technical and non-native-English academic writing, so results are framed as a self-check and never as proof of misconduct. Enable under Settings → AI Detection.
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
Visual analysis	3D reference-library graph (Obsidian-style), real per-paper citation graph, and a native-feeling document viewer with zoom + in-document find
Share & export	Self-contained HTML report, publish-to-web link (GitHub Gist), an animated video walkthrough, and RIS export for Zotero / EndNote / Mendeley
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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Feature Matrix (Web / Desktop / CLI / API)

RefChecker ships in four access methods that share one verification engine (ProgressRefChecker). The table below shows where each capability is available. The CLI column lists the exact flag — these match refchecker-webui check --help (CLI guide below). UI-interactive surfaces (in-app viewers, graphs, share video, author hovers) are web/desktop-only and are documented as such. For per-feature guides see docs/FEATURES.md; for multi-user setup see docs/MULTIUSER.md.

Legend: ✅ available · — not applicable to that surface · 🌐 needs a hosted/multi-user server.

Capability	Web UI	Desktop (Tauri)	CLI	API	Notes
Reference verification (S2 / OpenAlex / CrossRef / DBLP / ACL)	✅	✅	✅	✅	Core engine; identical results across surfaces
LLM extraction (Anthropic / OpenAI / Google / Azure / vLLM)	✅	✅	✅ --llm-provider	✅	--no-llm for regex/structural only
Hallucination detection (deep web search)	✅	✅	✅ --check-hallucinations	✅	Needs a web-search-capable provider; see Hallucination Detection
Inline-citation numbering/ordering check	✅	✅	✅ --check-citation-order	✅	Scheme-aware; abstains when unclear
Retraction screening (OpenAlex)	✅	✅	✅ --check-retractions	✅	Flags only references OpenAlex reports retracted
Gap-finder / co-citation suggestions	✅	✅	✅ --suggest-missing	✅	OpenAlex-resolved real works only
Enrichment (counts · abstract · claim/TL;DR · funding · author metrics incl. ORCID · h-index)	✅	✅	✅ on by default (--no-enrich)	✅	Mirrors the web/API default
Add-to-reference-list (dedup + renumbered list + tracked PDF diff)	✅	✅	—	—	Interactive editing surface
AI-generated-text detection (opt-in, advisory)	✅	✅	✅ --ai-detection {local,api} + --ai-detection-consent	✅	Opt-in + consent required; never proof of misconduct
Multi-detector compare + checkbox export (RAID-informed roster)	✅	✅	✅ --detectors key1,key2 · --list-detectors	✅	Per-detector scores shown honestly; no synthetic ensemble; uninstalled ⇒ abstains
Local databases for offline / faster verification	✅	✅	✅ --database-dir / --s2-db / …	✅	Same resolver across surfaces
Structured machine-readable output	✅	✅	✅ --json	✅ (JSON responses)	Progress to stderr, JSON to stdout
Bulk / batch checking	✅	✅	✅ (academic-refchecker --paper-list / --openreview)	✅	See Bulk Checking
Native PDF viewers (find · in-PDF citation links · color coding · pinch-zoom)	✅	✅	—	—	Interactive UI surface (R02/R28/R42)
Seen-library graphs (radial + Obsidian-style 3D) + per-paper citation graph	✅	✅	—	—	Interactive UI surface
Similar papers + "Cites & Refs" + common-works view	✅	✅	—	—	Interactive UI surface
Per-reference chat (full-text grounded, TL;DR fallback) + Summarize	✅	✅	—	—	Separate model selection per feature
Share / export (HTML · Markdown · PDF · DOCX · RIS · video)	✅	✅	—	—	Interactive share surface; CLI uses --report-file/--report-format
Author / journal hover cards (h-index · ORCID · guidelines)	✅	✅	—	—	Interactive UI surface
Live token / $ telemetry per LLM flow (R47)	✅	✅	—	✅ (per-request usage)	UI meter is web/desktop; usage is returned by the API
Accounts · Teams · realtime shared-batch presence (R26/R27)	🌐	🌐	—	🌐	Opt-in multi-user mode; see Multi-User Server
Support menu (email + open a GitHub issue)	✅	✅	—	—	In-app header menu

Single-user vs multi-user. Web/Desktop/CLI all run single-user/local by default — no login, no team, no presence. Accounts, Teams, and shared-batch presence light up only when you enable multi-user mode (set REFCHECKER_MULTIUSER=true and configure an OAuth provider, or use the in-app Accounts & Teams form with hot-reload). The CLI is always single-user and never makes a team/collaboration claim. Full setup: docs/MULTIUSER.md.

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Sample Output

Web UI

A completed check — summary health, the 2×2 article-tools grid (retractions · gap-finder · citation-numbering · chat & summarize), and AI-generated-text detection with a per-page breakdown:

Per-reference verification and enrichment — matched database, verified/DOI links, citation counts, and the Additional-Info bar (abstract · claim · topics · full link · add-to-library):

CLI — Startup banner

Running the CLI prints an environment + capabilities banner (colourised on a TTY, plain when piped). --help lists the full options and examples.

_{The banner prints to stderr (so machine-readable stdout like --report-format json stays clean). NO_COLOR=1 disables colour · FORCE_COLOR=1 forces it.}

CLI output — single-paper scan (errors, warnings, summary)

📄 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 output — 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.

Full CLI usage, flags, and examples are in the CLI section below.

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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

Show every flag (input, LLM, hallucination, AI detection, output, OpenReview)

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

refchecker-webui check — single-paper checker (web-parity flags)

The refchecker-webui command (installed with the [webui] extra) has two subcommands. With no subcommand it serves the Web UI / API (the historical behaviour); the check subcommand runs the same pipeline the web app uses (ProgressRefChecker) against a single paper from the terminal, exposing the web/API feature flags — hallucination check, inline-citation numbering/ordering, retraction screening, gap-finder suggestions, enrichment backfill, and opt-in AI-text detection. It reuses the real backend implementations (it never forks the verification, retraction, gap-finder, inline-citation, or AI-detection logic).

# Serve the Web UI / API (default — no subcommand needed)
refchecker-webui                     # http://localhost:8000
refchecker-webui serve --port 9000   # explicit subcommand form

# Check a single paper from the terminal (examples match `check --help`)
refchecker-webui check --paper 2406.01234
refchecker-webui check --paper ./paper.pdf --json
refchecker-webui check --paper ./refs.bib --check-retractions --suggest-missing
refchecker-webui check --paper 2406.01234 --check-hallucinations \
    --llm-provider anthropic --llm-model claude-3-5-sonnet-latest
refchecker-webui check --paper ./paper.pdf --ai-detection api \
    --ai-detection-consent --ai-detection-key $PANGRAM_KEY

# Multi-detector AI-text compare (only INSTALLED detectors run; rest abstain)
refchecker-webui check --list-detectors            # roster: installed vs. available
refchecker-webui check --paper ./paper.pdf \
    --ai-detection local --ai-detection-consent \
    --detectors desklib,e5-small-lora

Structured output (--json). A single JSON document is printed to stdout; all progress logging goes to stderr, so stdout stays machine-readable. The document always carries paper_title, paper_source, source_type, summary, and references, plus — only when you set the corresponding flag — citation_order (--check-citation-order), retractions (--check-retractions), suggestions (--suggest-missing), and ai_detection (--ai-detection).

Web/desktop-only — not on the CLI. The native in-app PDF viewers and in-PDF citation hyperlinks, the seen-library / similar-papers 3D graphs, the shareable per-check "video", and the author hover/pin profile cards are interactive UI surfaces, available only in the Web UI and the desktop (Tauri) build. The CLI makes no team / collaboration claim — it is always single-user/local.

Honesty notes (same as --help). No fabrication — every author / paper / DOI / count comes from a real resolved source, and checks abstain rather than emit a wrong badge. Cross-source enrichment backfill is on by default (pass --no-enrich to opt out). AI-generated-text detection is opt-in and advisory only (never proof of misconduct) — it requires --ai-detection plus an explicit --ai-detection-consent flag.

Run refchecker-webui check --help for the full, authoritative flag list.

---

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.

---

AI-Generated Text Detection

Opt-in and advisory only. AI-text detection is unreliable on academic, technical, and non-native-English writing, and on human text polished with AI. RefChecker frames every result as a low/medium/high likelihood band with a permanent disclaimer — never a binary verdict or proof of misconduct, and never a basis for an accusation, grade, or decision. Below ~300 words, or on equation/code/citation-heavy passages, it abstains (inconclusive).

When enabled (Settings → AI Detection), each checked article's body text is analyzed for AI-generated likelihood, in single and batch modes. Results include a confidence donut, AI / Mixed / Human probability pills, a page-by-page breakdown, Top AI / Top Human sentence lists, and advisory flagged passages you can open highlighted in the document — alongside the engine/model used and a permanent disclaimer.

Run mode. Settings → Run mode controls what a check actually runs:

Mode	Reference checking	AI detection
References only (turn AI detection off)	✅	—
Reference check + AI detection	✅	✅ (runs in parallel)
AI detection only	— (extraction & verification skipped)	✅

Detection engines (pick one in Settings)

Engine	What it is	Cost	Notes
Local model (default)	desklib/ai-text-detector (DeBERTa-v3, MIT) run offline via Transformers + PyTorch	Free	One-time model and runtime download, both installable from Settings → AI Detection; calibrated, reproducible; no data leaves your machine
LLM judge	Reuses your configured LLM provider (OpenAI/Anthropic/Google/Azure) with an anti-false-positive rubric	LLM tokens	Uncalibrated, so it is hard-capped at "medium" — it can never raise a standalone "high"
External API	Pangram or GPTZero	Per-word $	Requires an API key and explicit consent (your manuscript text is sent to a third party)

The local model needs an inference runtime (torch + transformers) that is not bundled, to keep the desktop app small. Click Install runtime under Settings → AI Detection to fetch it on demand (installed into the app's data folder and used without a restart), or install it yourself with pip install torch transformers. The LLM-judge and External-API engines need no runtime.

Multi-detector compare (RAID-leaderboard-informed roster)

Beyond the default desklib model you can install one or more open-source detectors and run them side-by-side. Each detector's verdict is shown on its own — there is no synthetic "ensemble truth"; disagreement between detectors is surfaced as signal. Detectors are installed on demand (never bundled), and an uninstalled detector abstains — it never reports a number. Heavy Tier-2 metric/zero-shot detectors are listed for honesty but are opt-in and not runnable in this build (real size / RAM warnings are shown so you understand why).

Key	Model	Arch	Tier	Size	License	Note
desklib (default)	desklib/ai-text-detector-v1.01	DeBERTa-v3-large	1	~870 MB	MIT	RAID leaderboard leader among open models
superannotate	SuperAnnotate/ai-detector	RoBERTa-Large	1	~1.4 GB	research/eval	#1 open-source on RAID (late 2024)
e5-small-lora	MayZhou/e5-small-lora-ai-generated-detector	e5-small + LoRA	1	~130 MB	MIT	tiny/fast/CPU-friendly (~89% acc)
mage	yaful/MAGE	Longformer	1	~570 MB	Apache-2.0	"Detection in the wild" (ACL 2024)
binoculars	paired causal LMs	metric zero-shot	2 (heavy)	~14 GB	see models	best at low FPR; opt-in, not runnable here
fast-detectgpt	GPT-Neo-2.7B scorer	metric zero-shot	2 (heavy)	~11 GB	see models	340× faster DetectGPT; opt-in, not runnable here
radar	TrustSafeAI/RADAR-Vicuna-7B	adversarial classifier	2 (heavy)	~13 GB	see card	robust to paraphrase; opt-in, not runnable here

Roster informed by the RAID benchmark (ACL 2024) (leaderboard · paper). In Settings → AI Detection you install/remove each detector (real size + license shown), run any subset, compare per-detector scores + per-sentence agreement, and checkbox-export only the detectors you select (MD / CSV / JSON). From the CLI:

refchecker-webui check --list-detectors            # roster: installed vs. available
refchecker-webui check --paper ./paper.pdf \
    --ai-detection local --ai-detection-consent \
    --detectors desklib,e5-small-lora              # only INSTALLED run; rest abstain

Usage & cost tracking

AI-detection work is metered in the same per-check token/$ badge under an "AI-generated-text detection" flow: the local model records the processed word count at $0; the API backends record words sent plus an estimated dollar cost; the LLM-judge records real input/output tokens and their cost.

Graph 2nd-degree expansion

In the Graph tab, the 2nd-degree expansion has a "Refs only" vs "+ AI-gen" toggle. With "+ AI-gen", each expanded article also gets an AI-likelihood ring (red = high, amber = medium), estimated locally from its abstract (free, offline). Abstracts are short, so most come back inconclusive — this is an advisory signal, never a full-text analysis.

Sources & credits

The detection engines build on these open-source projects and services:

• desklib/ai-text-detector-v1.01 — DeBERTa-v3 detector (MIT), the bundled local model
• harshaneel/humanize — the ai-check forensic rubric (MIT) adapted for the LLM-judge prompt
• distil-labs/distil-ai-slop-detector — the "small quantized classifier in-app" concept (Apache-2.0)
• Zero-shot research: Binoculars and Fast-DetectGPT
• API services: Pangram and GPTZero

On the reliability of detectors for academic/non-native-English text, see Liang et al., arXiv:2304.02819.

---

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.

---

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, and every request runs as a built-in local admin. Multi-user mode is opt-in: it turns on only when you both set REFCHECKER_MULTIUSER=true and configure at least one OAuth provider's client ID and secret (Google, GitHub, or Microsoft). Setting the flag alone — with no provider credentials — leaves the app in single-user mode and shows no login screen. Once at least one provider is configured, the Web UI gates behind a login page that renders a sign-in button only for the providers the server reports at /api/auth/providers, and every API route requires a valid session.

If the server has LLM provider environment variables such as ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, or AZURE_OPENAI_API_KEY, the Web UI exposes those providers as selectable server-environment configs without revealing the secret. Users can still enter their own keys to override the server key for their browser session; user-entered keys are 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 — only providers whose ID *and* secret are set
# appear as login buttons. Microsoft uses the MS_* prefix.
GOOGLE_CLIENT_ID=...
GOOGLE_CLIENT_SECRET=...

GITHUB_CLIENT_ID=...
GITHUB_CLIENT_SECRET=...

MS_CLIENT_ID=...
MS_CLIENT_SECRET=...

# Optional — by default the callback URL is derived from SITE_URL as
# <SITE_URL>/api/auth/callback/{google,github,microsoft}. Override per provider
# only if you registered a different redirect URI:
# GOOGLE_REDIRECT_URI=https://<your-domain>/api/auth/callback/google
# GITHUB_REDIRECT_URI=https://<your-domain>/api/auth/callback/github
# MS_REDIRECT_URI=https://<your-domain>/api/auth/callback/microsoft

# 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.

---

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-3.1-flash-lite-preview
Azure	AZURE_OPENAI_API_KEY	gpt-4.1
vLLM	(local)	meta-llama/Llama-3.3-70B-Instruct

When running the Web UI, provider keys present in the server environment are added automatically as selectable LLM configurations in both single-user and multi-user mode. The key value is not returned to the browser; users can still enter a browser/session key to override the server environment key for their own run.

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:

• Feature guide & access-method matrix — per-feature guides across web / desktop / CLI / API, with CLI usage examples that match refchecker-webui check --help.
• Multi-user & Teams setup — enable accounts, Teams, and presence from the in-app form (hot-reload) or via environment variables.
• 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.