Methodology

Every AI-generated review on AIPR is a starting point for a human reviewer, not a final verdict. The model produces scores and per-dimension comments; a reviewer reads the draft, edits whatever needs editing, and decides what the author sees. The AI never sends output directly to an author. Reviewers are free to overrule any score the model assigned. What the author receives is the reviewer's final version, not the model's.

Borderline scores, citation concerns, and suspected misconduct are flagged for the reviewer's attention but are never decided by the AI alone.

A longer version of why this matters lives on the purpose page.

Every week we look at thousands of newly published research papers. Each selected paper is read in full and graded across five scoring dimensions by the model, producing a structured draft for a human reviewer to take forward.

The grading pass produces per-dimension scores with a written rationale for each one, so the reviewer (and later the author) can see why the model assigned what it did. Reviewers can revise scores, rewrite rationales, or send the draft back for another pass before it is finalised.

Citations are checked against external academic databases to verify that referenced works exist and are attributed correctly. Within a given weekly cohort, papers are also compared against each other so that the relative ranking reflects differences the model can defend, not differences the model happened to imagine.

Once a reviewer approves the review, the paper and its full evaluation breakdown can be published to the leaderboard. Readers see the reviewer-approved version, not the raw model output.