ResearchRabbit

Automatically generates interactive visual maps showing connections and relationships between academic papers, research clusters, and citation networks. Reveals hidden patterns and thematic groupings across literature in a graphical interface rather than traditional list formats.

Generates personalized academic paper recommendations based on reading history, saved papers, and research interests. Uses machine learning to identify relevant papers users are likely to find valuable without requiring manual search queries.

Monitors academic databases for new papers matching user-defined research interests and automatically sends notifications when relevant work is published. Reduces noise compared to traditional alert systems by using AI to filter for genuine relevance.

Tracks citations between papers, identifies which papers cite specific works, and analyzes citation patterns and impact. Provides visibility into how research builds upon and references other work.

Creates and maintains user research interest profiles based on saved papers, reading history, and explicit topic definitions. Enables personalized experiences across recommendations, alerts, and content discovery.

Extracts and displays comprehensive metadata for academic papers including authors, publication dates, abstracts, keywords, and journal information. Provides structured information about papers in an accessible format.

Allows users to save, organize, and manage collections of papers within ResearchRabbit. Enables creation of custom collections and folders for different research projects or topics.

Enables users to search for papers by topic, keywords, authors, or other criteria and discover relevant literature through AI-enhanced search. Provides more intuitive discovery than traditional database search interfaces.

Enables researchers to share collections, research interests, and literature maps with collaborators. Facilitates collaborative literature review and research exploration.