AI Review, agent by agent
The 8 agents behind AI Review
AI Review splits a manuscript review into 8 specialist checks that run at the same time. Of those, 7 are language-model reviewers, each calibrated on real peer reviews for its own specialty, and Prior Publication is a deterministic lookup across 7 external sources and our 4.5M-paper local library. A synthesis step then writes one report. A full review costs 30 credits.
What each one checks
One narrow job per agent
Each language-model agent carries only its own rubric, so it can point at specifics — a section, a table, a sentence — instead of generalising. The Prior Publication agent uses no language model at all.
Methodology
Language model · calibrated on real reviewsWeight ×2.0 in the overall scoreAudits study design, statistical power, and analytical choices against field-specific rigour standards (CONSORT, STROBE, PRISMA).
What it looks for
- Study design against reporting standards such as CONSORT, STROBE and PRISMA
- Sample-size justification, statistical power, and whether the test suits the data
- Red flags: data leakage, pseudoreplication, HARKing, causal claims from correlational data
Formulas & Equations
Language model · calibrated on real reviewsWeight ×1.2 in the overall scoreVerifies mathematical derivations, checks dimensional analysis, and flags algebraic errors.
What it looks for
- Dimensional consistency of each equation's terms
- Undefined variables and one symbol used for two quantities
- Values in the text that disagree with the equations or tables, with a suggested correction
Originality
Language model · calibrated on real reviewsWeight ×1.5 in the overall scoreChecks the manuscript's novelty claims against papers in our 4.5M-paper local library and flags possible overlap and self-plagiarism signals.
What it looks for
- Novelty claims set against similar papers in the local library
- Incremental versus genuinely new contribution, and overclaiming such as "first ever"
- Self-plagiarism, salami-slicing and duplicate-submission signals in the text
Literature Coverage
Language model · calibrated on real reviewsWeight ×1.2 in the overall scoreEvaluates citation completeness, missing seminal references and self-citation balance, with a live OpenAlex snapshot of the field (volume, top venues, peak year) for context.
What it looks for
- Missing foundational and directly competing work
- Self-citation balance and recency for the field
- Miscitation and weak sources for key claims
Reads the reference list it can see. With LaTeX, attach your .bib or .bbl so it reads that too.
Reproducibility
Language model · calibrated on real reviewsWeight ×1.0 in the overall scoreInspects code availability, dataset accessibility, and sufficiency of methods detail for independent replication.
What it looks for
- Where the data and code live: a repository, DOI or accession number, not "available on request"
- Software versions, hardware, random seeds and dataset splits
- Trial registration, protocol and analysis plan for clinical work
Clarity & Language
Language model · calibrated on real reviewsWeight ×0.8 in the overall scoreAssesses readability, structural flow, and adherence to scholarly writing norms.
What it looks for
- An abstract with background, objective, methods, results with numbers, and a conclusion
- Hedging: overclaims such as "proves", and needless over-hedging
- Consistent terminology, over-long sentences, undefined acronyms and section flow
Figures & Tables
Language model · calibrated on real reviewsWeight ×0.8 in the overall scoreChecks figure quality, caption completeness, and appropriateness of visual encodings.
What it looks for
- Self-contained captions: units, error-bar type, n per group, the test used
- Chart type for the data, colour-blind-safe palettes, resolution
- Numbers in the text that disagree with a figure or table, and image-integrity flags
Sees the figure images embedded in a Word file, or the figure files you attach to LaTeX; from a PDF it works from the captions and text.
Prior Publication
Deterministic lookup · no language modelReported on its own, not averaged inA deterministic lookup, not a language model: fans out in parallel to 7 external sources — CrossRef, PubMed, arXiv, bioRxiv, medRxiv, Europe PMC and Unpaywall — and our local library to detect prior publication and duplicate submission.
What it looks for
- An existing record whose title and abstract closely match yours
- Searched in parallel: 7 external sources and our 4.5M-paper local library
- A likely or possible prior publication is flagged in the report, with a link to the matching record when the source gives one
Reads the title and abstract only.
From 8 reports to one
How the agents become one report
- 1
They start together
All 8 agents begin at the same time, so this stage takes about as long as the slowest single agent, not the sum of all of them.
- 2
Each language-model agent is calibrated first
Before it reads your paper, each of the 7 is shown real peer reviews retrieved for its own specialty from a corpus of 69K+ reviews collected from 19+ open-review platforms — reviewers' critiques first, with a mix of decisions and sources — and told to hold its concerns to that standard.
- 3
It gets context on the field
When the local library has them, each also receives research gaps that similar papers have already stated in its area, plus a live OpenAlex snapshot of the field: how much has been published, the top venues and the peak year.
- 4
Scores become one weighted score
Methodology counts ×2.0 and Originality ×1.5, the two heaviest weights; Prior Publication is reported on its own and not averaged in. Any language-model agent scoring 2 or below caps the overall at 4.0. The verdict follows the score: Accept from 8.5, Minor Revision from 6.5, Major Revision from 4.0, Reject below that.
- 5
A synthesis step writes one report
One report: the verdict, a recommendation that cites what the agents found, the issues to address first, the paper's strengths, any formula corrections and the reproducibility gaps.
Run all 8 on your manuscript
Upload a PDF, Word or LaTeX file. A full review costs 30 credits, charged once for every agent and the synthesis. Credits come in one-time packs of 50 and never expire; there is no subscription.
Questions