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Technical checks to support human judgment

From author self-review to editorial triage at scale—objective, verifiable checks that free reviewers and editors to focus on what only they can judge.

Integrity Checks

→Hallucinated, Retracted & Self-Citations
→AI-Generated Text
→Fatal Flaws

Rigorous Review

→Data & Code
→Analysis
→Scope

Evidence from 145,000+ review comments

What we found across computer science, social science, and life science research.

90%

of R3 feedback could undermine a major claim

98.5%

accuracy separating real citations from AI-fabricated ones

1st

among AI reviewers in agreement with humans

See the benchmarks

What researchers and editors are saying

"The final decision on whether to accept a paper depends on factors only an experienced human can judge. But all the upstream work—technical checks and data verification—is perfectly suited to AI. That's where Reviewer3 shines."

Prof. Adriano Aguzzi

University of Zurich

"Reviewer3 exceeded our expectations as a tool for improving manuscripts. We're hoping this will improve and expedite review outcomes when we submit our papers to academic journals."

Prof. Matt Tegtmeyer

Purdue University

"Tools like Reviewer3 can help ensure reviews are unbiased, thorough, and detail-oriented, raising the overall quality and consistency of peer review."

Dr. Antonio Cembellin Prieto

Arc Institute

Built for how you work

Whether you're preparing a submission or managing editorial workflows, Reviewer3 adapts to your needs.

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Identify methodological gaps and strengthen your manuscript before submission. Unlimited revisions with Premium.

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For reviewers

Bring a second set of eyes to a manuscript you have been asked to review, and write your report with more confidence.

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For editors

Speed up first-pass triage at scale—surface fatal flaws, citation problems, and AI-generated text before manuscripts reach external reviewers.

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Enterprise solutions for research offices, graduate programs, and grant administration.

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AI Peer Review Checks What's Checkable

An AI reviewer reads a paper the way a referee would. It breaks the paper into the claims it makes, then checks the evidence offered for each one. Does the analysis support the conclusion? Do the claims agree with each other? Are the cited sources real? It won't tell you if the work matters. It catches the problems a human reviewer shouldn't have to spend their time on.

We Verify. We Don't Generate.

Most AI writing tools produce text. Reviewer3 checks text that already exists, and that difference decides what it's good for. Ask a model to “review this paper” and it'll hand you something review-shaped whether or not it found anything, because producing plausible text is the job it's doing. Verification means actually going and looking. Searching for the cited source. Checking claims against the data reported. Saying nothing is wrong when nothing is wrong.

What It Can't Do

It can't tell you whether a contribution is novel, or whether a field will care. That takes knowing what a community values, and it's still a human question. What it's good at is the work where nothing can be sampled. Every reference verified instead of a few. Every claim checked against every other.

Claims First, Then the Evidence for Each

A manuscript is decomposed into the claims it makes, and every claim is checked against the evidence offered for it. Whether the analysis supports the conclusion. Whether the statistics hold. Whether the claims contradict each other or the data reported. Separately, a reference chain pulls out every citation, goes looking for a real source, and decides whether the match is genuine. Stages run in parallel where they can and wait where one needs another's result, and an editor stage pulls all of it into one report, ordered by what matters most.

Who Uses It

Researchers run it on their own work before they submit, to see what a reviewer is likely to raise while there's still time to do something about it. Journals and editorial teams run it on incoming submissions, so a screening decision comes with the methodology and citation checks already done. Same pipeline, different modes.

How do you know it works?

Because we published the tests. The reference checker is measured against a dataset of real and fabricated citations where the answer is known in advance, and ReviewBench compares over 145,000 human and AI review comments. Both are on the benchmarks page, with the datasets and methods, so the claims can be checked rather than taken on trust.

Why does peer review need help at all?

Because the volume has outrun the reviewers. Published articles have roughly doubled since 2010, while 20% of scientists carry up to 94% of the reviewing and fewer than half of review invitations are accepted. That arithmetic does not resolve itself. Our write-up on AI peer review sets out the numbers behind the strain, why we were skeptical that AI could help, and how the review stages were built and measured.