Built onFactReviewarXiv:2604.04074 ↗
Peer review, with evidence.
Every claim, traced to its source.
Upload an ML paper. We extract every claim, locate it in the literature, verify each citation, and return a structured report you can audit line by line.
Encrypted in transit & at restNever used to train AIFree for academics
See an example review →no signup needed
01 / Pipeline
How it works
01
Parse
Extract sections, tables, citations and figure regions from the PDF.
02
Extract & locate
Pull every empirical, theoretical and reproducibility claim. Pin each to a section.
03
Verify
Cross-check against literature, RefChecker and (optionally) executed code.
04
Report
Each claim gets a 5-label verdict, evidence list and a one-glance teaser figure.
02 / Differentiation
What you get that others don't
FactReview this product | Stanford Agentic | Manusights | Reviewer3 | |
|---|---|---|---|---|
| Citation verification, no cap | ✓yes | —no | ~partial | —no |
| Literature positioning + novelty verdict | ✓yes | ~partial | —no | —no |
| 5-label claim ledger w/ evidence links | ✓yes | —no | ~partial | ~partial |
| Teaser figure (poster-style overview) | ✓yes | —no | —no | —no |
| Bilingual output (EN / 中文) | ✓yes | —no | —no | —no |
03 / Trust
What we promise
01Your paper is encrypted in transit and at rest. Always.
02We will never use your paper to train a model. Ever.
03Your review URL is a 32-byte secret — unguessable, never indexed.
04 / FAQ
Frequent questions
Is FactReview the same as the FactReview paper?
We're built on top of it. The pipeline is from Xu et al. (2026); we wrap it in a web product. Every review is stamped with the backend version that produced it.
Can I use this for an OpenReview submission?
Yes — but check your venue's disclosure rules. We surface that reminder in the workspace and in every email footer.
Why not give me a single accept/reject score?
Because that's the part you should decide. We collect evidence; you read it. AI in peer review is most useful as an evidence collector, not a judge.
What languages do you support?
Input must be an English ML paper. Output ships in EN and 中文 — evidence quotes always stay in the original English so they remain traceable.