Paper search that reaches behind your library login
Your AI sweeps arXiv, Scholar, and PubMed, opens the full PDFs through your library access, and returns an annotated reading list.
“Build me an annotated reading list on protein-design diffusion models from the last 2 years. Read the full papers, not abstracts.”
Diffusion priors for de novo protein design
NeurIPS 2024
Closest match to your topic, open-access preprint
A systematic review of CRISPR base-editing safety
Cell Reports 2025your access
Behind your library proxy, read in full
Retrieval-augmented generation: an evaluation survey
ACL 2025
Most-cited in the last year on your filter
From one prompt to a sourced reading list
Connect the AI you already use, whether ChatGPT, Claude, or Gemini, to your browser. From one prompt it runs four steps, and reads the full text through the access you already have.
- 1
Point your AI at your own browser
Link ChatGPT, Claude, or Gemini over MCP. It gains the same Chrome you read in, with your arXiv tabs, your Google Scholar, and your university library proxy already signed in.
- 2
Describe the question, not just keywords
Give it the topic, the venues, and your filters, like last 2 years, most-cited, reviews only. It searches arXiv, Scholar, PubMed, and OpenReview live, never a stale index.
- 3
It opens the full texts through your access
For papers behind a publisher wall it uses the institutional or personal access you already have, opens the PDF, and actually reads the methods and results.
Abstract-only search never gets past the paywall page.
- 4
It hands back an annotated reading list
Each paper comes with a takeaway, the method, why it’s relevant, and a direct link to the page or PDF it opened, so every line is traceable to a source.
Your AI wants to save a reading list of 6 papers to your notes.
Save reading list (6 papers) to your notes
Protein-design diffusion models · last 2 years
Each with a takeaway, method, and source link
Every paper is sourced. Saving stays yours.
A reading list is only useful if you can trust it. Each item links to the page or PDF the agent actually opened, and writing it to your notes or a shared doc works like every consequential Actionbook action: you see exactly what it wants to do before it commits.
- Linked to the source: Title, authors, venue, and DOI come from the page it read, so you can click through to confirm any claim.
- You approve the save: Writing the list to your notes surfaces for your approval first, in plain language.
- Watch it work: Every search and PDF opens in your own Chrome tab, so you can take over at any point.
Built for how researchers actually read
One workflow, tuned to whatever you’re reviewing this week.
PhD students
Turn a weekend of a literature review into one prompt: it sweeps the venues, opens the gated reviews through your uni login, and returns an annotated reading list.
R&D engineers
Track a fast-moving field. Ask for the most-cited papers since last year, get each method summarized with a link, and stay current without reading every PDF yourself.
Clinicians
Check the evidence behind a decision. It pulls systematic reviews from PubMed and your subscription journals, with each study’s sample size and conclusion sourced.
Full text via your own access
Most citable reviews live behind a publisher wall. It opens each one through the library proxy or subscription you already log into, reading the methods and results, never bypassing a paywall.
Writers & analysts
Fact-check a claim against the primary source instead of a secondhand summary. It opens the actual paper and quotes the line it read, with the link to confirm.
Prompts to start your literature review
Copy a prompt, paste it into your AI, and Actionbook runs it in your own browser session.
Build me an annotated reading list on diffusion models for protein design from the last 2 years. Pull from arXiv and OpenReview, open the full papers, and give each a one-line takeaway, the method, and a link.
Open these 5 DOIs through my university library access, read the full text of each, and summarize the methods and key findings with the sample size where reported.
On Google Scholar, find who cited this paper in 2025, open the three most relevant citing papers in full, and tell me what each found and whether it agrees with the original.
Compare the evaluation setups of these 3 papers on retrieval-augmented generation. Open each PDF, pull the datasets, metrics, and baselines, and put them in one table with links.
Search PubMed for systematic reviews on GLP-1 agonists and cardiovascular outcomes from the last 18 months, then give me a sourced summary with each study’s sample size and conclusion.
Find the most-cited 2025 papers on mechanistic interpretability across arXiv and OpenReview, read the abstracts and the methods sections you can open, and rank them by relevance to my work.
Frequently asked questions about AI research paper search
What researchers ask before letting an AI near their library access and reading lists.