ChicagoHAI/OpenAIReview

OpenAIReview — AI-Powered Academic Paper Reviewer

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How do authors want to use AI for review?

A survey of researchers who compared AI-generated scientific reviews with journal-agnostic human peer review reveals that they overwhelmingly prefer using AI as a self-checking tool before submission rather than as a replacement for human reviewers. It encourages an “author-centric” model in which AI helps researchers improve their manuscripts before they are reviewed by their peers.

AI-powered Literature Review & Synthesis

Recording from my workshop last week on smarter literature review with AI

Adil on Twitter / X

being assigned to review AI generated slop papers is very frustrating. the way this is supposed to work is that first you take the time to write something and then i take the time to read it. if you don't do the first part then i shouldn't have to do the second part.— Adil (@adilsoubki) August 26, 2026

AI-assisted Reviewing is Necessary and Should be Open

Peer review is facing a death spiral. AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open.

AI-assisted Reviewing is Necessary and Should be Open

Peer review is facing a death spiral. AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open.

AI reviewers are here — we are not ready

Nature - Artificial intelligence promises rapid and polite feedback on papers — but we must first review the reviewer.

Socialize the Plan

On AI and code review.

OpenAccess.ai — Rigorous Open Access Publishing

$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.

AI peer review goes pro

A what we're reading spotlight

Memorize details from AI research papers

The second brain of AI researchers for machine learning, computer vision and natural language processing paper reviews.

AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.

Can AI agents conduct open-ended AI research? Early evidence from two case studies

Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\&D automation. An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper's original authors grade its output. We call these shadow evaluations. We ran shadow evaluations on two unpublished NeurIPS 2026 submissions, giving frontier agents six days and thousands of dollars of compute. The agents completed all of the engineering without human help, yet could not make substantial progress towards answering the research questions. As a result, both papers were unambiguously rejected by the authors. We identify five recurring failure modes: poor judgment about the bar for publishable research, uncreative responses to shortcomings in the research design, ineffective backtracking from dead ends, poor resource awareness, and instruction drift. A robustness check with a second model and scaffold reproduced these failures. We release the expert reviews, survey responses, agent repositories, and logs. Our results provide early evidence that today's agents can do the engineering of AI research, but struggle with critical parts of the research lifecycle.