ResearchRabbit is an AI literature discovery and mapping tool that finds related papers, authors, and topics from a seed. Build interactive citation and co-citation maps, explore author networks, and see how a field evolves on a timeline. Create collections, add notes, and share with collaborators; set alerts to track new publications. Deep discovery scans abstracts and full texts to surface findings missed by keyword search—helping students and researchers turn questions into evidence fast.
Start from one or more seed papers and watch ResearchRabbit surface related studies through citation trails and co-citation patterns. It builds an interactive map that shows clusters, influential works on a timeline, and hidden bridges between subtopics missed by keyword search. Click any node to preview abstracts, open publisher pages, save to collections, and annotate—turning a messy reading list into a navigable picture of the field. Filters help you focus on core papers first, then expand.
Trace influential authors and labs with co-authorship and citation views that reveal schools of thought. See who collaborates, which groups bridge topics, and how contributions change over time. From an author node, open recent papers, add them to a project list, or follow to get alerts. This perspective helps you find mentors, competitors, and emerging teams faster than manual searches through profiles and CVs—and avoid overlooking key names when scoping a field.
Curate papers into named collections with notes, tags, and priorities, then share with collaborators for joint reviews. Alerts notify you when new papers match your topics or when followed authors publish, so you stay current without constant searches. Lists act like living bibliographies you can export to citation managers or sheets, keeping sources organized and decisions traceable as projects evolve from scoping to manuscript. Inline annotations capture questions and to-dos for the next pass.
When questions get specific, use deeper discovery to scan abstracts and available full texts for methods, outcomes, and limitations. Ask Paper lets you interrogate a single study—sample size, effect direction, population—returning answers with citations to exact sections. This reduces time spent skimming PDFs and helps verify relevance before detailed reading. Together, these modes surface evidence missed by keyword search and keep claims tied to sources you can revisit during writing or review.
Capture papers as you browse with the browser extension, then open them in ResearchRabbit to map and file immediately. Use university access where available to sign in via SSO and share collections in classes or labs. Exports fit citation managers and spreadsheets so references move cleanly into writing tools. Because projects live in the cloud, your maps and lists sync across devices, letting teams review evidence together without emailing PDFs or maintaining parallel trackers.
Students, postgrads, and researchers who want faster, better-documented literature reviews. Ideal for thesis scoping, grant prep, systematic mapping, and staying current in fast-moving fields. Great for labs and classes that need shared collections and alerts, and for writers who must cite confidently. If your workflow bounces between Google, PubMed, and PDFs, ResearchRabbit centralizes discovery, tracking, and verification without complex setup.
Replaces tab-hopping and manual reference chasing with a visual, traceable workflow. It solves three pains: missing key papers because keyword search is shallow, losing context about who cites whom, and disorganized sources scattered across spreadsheets and folders. By mapping citations and co-authorships, saving living lists, and alerting you to new work, ResearchRabbit makes evidence gathering faster and more defensible—so you spend more time analyzing than hunting.
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