we recommend using uv, and there are additional guidelines for best results with PDF inputs
Science should be machine-readable - Marginal REVOLUTION
One of the leading tasks of our time: We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate […]
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-powered Literature Review & Synthesis
Recording from my workshop last week on smarter literature review with AI
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.
Local-First AI Inference: A Cloud Architecture Pattern for Cost-Effective Document Processing
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
Towards Automating Scientific Review with Google's Paper Assistant Tool
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT benchmark. Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.
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.
Refine
I recently tried refine, an AI tool for refining academic articles, developed by Yann Calvó López and Ben Golub.
The new way we’ll do science
Papers should become human-readable views over a graph of data, tools, results, and certificates.
AI’s Growing Role as Scientific Peer Reviewer | Stanford HAI
Stanford computer scientist James Zou is exploring how AI can accelerate scientific research and peer review. His finding: AI excels at spotting gaps, but judgment calls still need humans.
Elicit: AI for scientific research
Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.
Elicit: AI for scientific research
Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.
Peer review is facing a death spiral, and AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open. We built OpenAIReview: open AI reviewing for everyone, for the cost of a coffee. openaireview.github.io/blog.html 🧵
AI-assisted Reviewing is Necessary and Should be Open
openaireview.github.ioWhat if paper reading and reviewing was interactive and visual? Tools today assume a very passive way of engaging with a paper: you open a PDF and read linearly, top to bottom. But most of us already break that assumption: results first, figures before text, abstract then conclusion. 🧵 #scisci
