ResearchRabbit

ResearchRabbit is a web-based application designed to assist researchers, students, and academics in conducting literature reviews more efficiently. The platform leverages artificial intelligence to help users discover relevant academic papers, visualize relationships between publications through citation maps, and stay updated on emerging research trends. Instead of relying on simple keyword searches or static article lists, ResearchRabbit allows users to start with a single paper of interest and then expand outward by exploring its references, citations, and related works. The system uses algorithms to recommend papers that are conceptually similar or frequently co-cited, enabling users to uncover connections they might otherwise miss. One of the core functionalities is the interactive citation map. Users can add papers to a personal collection, and ResearchRabbit generates a visual network showing how those papers are linked through citations. This map can be explored by zooming in or out, clicking on nodes to view details, and branching out to new papers. The map also color-codes papers by publication year or other user-defined categories, making it easier to identify the evolution of a research topic. ResearchRabbit also includes a smart recommendation engine. As users build their library, the system continuously suggests new papers based on the content and citation patterns of the existing collection. These recommendations are not merely based on keywords; they take into account the full citation graph and the user’s interaction history. Users can rate suggestions or dismiss them, helping the AI refine future recommendations. Another important feature is the ability to create and manage multiple collections or “projects.” Each project can contain its own set of pa