How Science AI Journal reviews your paper

1. Upload

Upload a PDF, Word (.doc/.docx) or LaTeX (.tex) file. We review manuscripts from any scientific discipline — the agents are calibrated across engineering, medicine, life sciences, physical sciences, CS, mathematics, social science, environmental science, and economics.

• Images embedded in a Word file are extracted for the Figures & Tables agent; with LaTeX you can attach the figure files.
• You keep all copyright; your manuscript is never added to the training corpus.
• 30 credits per review, from one-time credit packs — no subscription.

2. Parallel pre-flight: prior publication detection

Before any review agent runs, the title and abstract fan out in parallel to 7 external sources — CrossRef, PubMed, arXiv, bioRxiv, medRxiv, Europe PMC and Unpaywall — and to our own 4.5M-paper local library, with a 12-second timeout per source.

We look for fuzzy title and abstract overlap. Matches above 60% word overlap are flagged as likely prior publication and shown in your report, with the matching record linked when there is one. The check is informational: the review continues either way, and the judgement is yours.

3. Eight specialised agents, in parallel

All eight start at the same time, so the wall time is roughly the slowest single agent rather than the sum of them. Each language-model agent carries only the rubric it needs, and only the calibration examples matching its domain — one narrow prompt per concern rather than one prompt asked to hold every concern at once. We have not published a measured agreement rate against human editorial decisions; what we have measured is on the benchmarks page.

• Methodology — study design, power, CONSORT/STROBE/PRISMA compliance.
• Formulas & Equations — derivations, dimensional analysis, algebra.
• Originality — novelty claims checked against the 4.5M-paper local library.
• Literature Coverage — missing seminal refs, over-reliance on self-citation.
• Reproducibility — code availability, dataset access, methods sufficiency.
• Clarity & Language — IMRaD adherence, hedging, undefined acronyms.
• Figures & Tables — readability, colourblind safety, caption completeness.
• Prior Publication — a deterministic lookup (not a language model) across 7 external sources and our local library.

4. Synthesis

A ninth pass integrates every agent's report into a single editorial recommendation — accept, revise, reject — with a numeric score and a line-by-line reviewer report. The full report is yours: every agent's findings, not a summary, copyable as Markdown, JSON or plain text. If our publication route opens, the plan is for that report to travel with the paper rather than sit behind a curtain.

What we will not claim

We do not replace human peer review where the stakes demand it — drug trials, regulatory submissions, grant panels. We do not outperform a careful, well-resourced human reviewer on nuanced theoretical work. We do not generate novel scientific insight. We review.

What AI Review is for: a competent, fast, transparent first pass before a manuscript goes to a journal — so the human referees who read it next spend their time on the science rather than on the fixable problems.