Paid tool · $5 per review
Paper Review
A senior-reviewer-level red team for your manuscript. Catches the blind spots reviewers will flag — before they do. Four-persona adversarial debate, live citation cross-check, figure-integrity scan. Delivered in under 10 minutes.
How the review works
- Layer 1 — Figure-integrity vision scan. Every page is rendered and reviewed for digital manipulation concerns, figure-vs-text contradictions, and presentation issues (broken y-axes, missing error bars, color-only encoding). Hedged language; flags concerns to investigate, never accusations.
- Layer 2 — Live citation cross-check. Every reference is verified against CrossRef. Dead DOIs, hallucinated citations, and weak title matches are surfaced individually so you can audit them before submission.
- Layer 3 — Adversarial four-persona panel. Methodology Critic, Statistical Skeptic, Data Integrity Officer, and Editor-in-Chief each red-team the paper in parallel. Findings are merged with a cross-persona consensus filter so issues raised independently by multiple personas surface first.
- Layer 4 — Rectification synthesis. A final pass produces the structured report you'll act on: critical blind spots, data-to-claim contradictions, an A/B/C rectification checklist, and an honest novelty estimate.
What does the report look like? View sample
# Manuscript Review
## Critical Blind Spots
- **Hyperparameter search asymmetry favors the proposed method.**
Quoted: "We tuned learning rate and batch size on the validation
set; baselines used reported defaults."
Surfaced by: Methodology Critic, Statistical Skeptic (consensus).
Fix: Match search budget for each baseline; report the protocol
in the methods section.
- **Sample-size justification missing for Experiment 2.**
Quoted: "We collected 24 participants ... yielding sufficient
statistical power."
Surfaced by: Statistical Skeptic.
Fix: Provide an explicit power calculation with assumed effect
size and the source of that assumption. With n=24 and the
reported effect, post-hoc power is below 0.6.
## Data-to-Claim Contradictions
- Claim: "Our method beats all baselines by a wide margin."
Evidence: Table 2 shows the proposed method tied with Baseline C
within reported standard errors on 4 of 7 datasets.
## Rectification Checklist
- [A] Add hyperparameter search protocol to methods (page 5,
Section 3.2). Match search budget across all baselines.
- [A] Replace "wide margin" framing in abstract and Section 5
with what the data actually shows.
- [B] Add per-seed variance to all results tables.
- [B] Reference [17] does not resolve to a CrossRef entry —
re-verify the citation.
- [C] Figure 3's y-axis breaks 0–60 then jumps to 90; redraw on
a continuous scale.
## True Novelty Estimate
The core architectural contribution is genuinely novel and not
covered by [11] or [23]. The empirical comparison, as currently
framed, oversells the gap. Marginal advance: the proposed mechanism
is interesting but the published evidence does not yet warrant the
strength of the abstract's claim.
## Reference Verification Summary
47 of 52 references verified against CrossRef. 3 had weak title
matches (re-check [12], [17], [31]). 2 had no DOI and could not be
located by title — verify these are correctly cited.
What you get
- One review of one manuscript (up to 20 MB PDF, ~100 pages).
- Markdown report you can download.
- Delivered in under 10 minutes via a polling status page.
- If you're unhappy with the result, email ben@purplelink.llc for a full refund.
Frequently asked questions
What does the review include?
Four layers in sequence: a Claude Vision pass over your figures and tables, a live CrossRef cross-check of every reference, an adversarial four-persona reviewer debate (Methodology Critic, Statistical Skeptic, Data Integrity Officer, Editor-in-Chief) with consensus filtering, and a synthesis pass producing critical blind spots, data-to-claim contradictions, a prioritised rectification checklist, and a true-novelty estimate.
How long does it take?
Typically 4-8 minutes for a 20-page manuscript. You don't need to keep the tab in the foreground — the status page polls until the review is ready and renders it inline.
What about my privacy? Is the manuscript stored?
Your manuscript is sent to Anthropic's Claude API for analysis. Anthropic retains inputs for up to 30 days for abuse monitoring, and does not use them for model training. Purplelink itself does not store your manuscript or the resulting review after delivery: the review is deleted from our servers the moment you retrieve it on the status page. Save the result locally before closing the tab.
What domains does it cover?
You pick one of five domain profiles at upload: Machine Learning, Biomedicine, Psychology and Social Science, Chemistry and Materials, or General. Each profile loads domain-specific attack vectors so the reviewers focus on the failure modes that matter for your field.
Is this a replacement for peer review?
No. Treat it as a first-pass red team before you submit — a way to catch the obvious problems a real reviewer will flag before they do. The review is advisory only; you remain responsible for every decision about your manuscript.
What if the review misses something or makes a mistake?
AI reviewers can be wrong. The output flags concerns with hedged language and quotes the manuscript when challenging a claim, so you can verify every finding against your text. If a specific result reads as low-quality, email ben@purplelink.llc — we'll refund the $5.
What file format?
PDF up to 20 MB. If you're submitting LaTeX, compile to PDF first (our free LaTeX-to-PDF tool works).
What about clinical or patient data?
Do not submit unredacted patient data. The manuscript will be sent to Anthropic's API with 30-day retention. For papers containing identifiable health information, redact before upload.