H-MAPS: Hierarchical Memory-Augmented Proactive Search Assistant for Scientific Literature
For researchers reading scientific papers, H-MAPS reduces cognitive load by proactively retrieving relevant literature based on user profile and reading behavior.
H-MAPS is a proactive literature search assistant that uses a three-layered hierarchical memory to resolve context ambiguity in scientific reading. It generates user-specific questions and retrieves relevant literature on-device, demonstrated with two researchers reading the same paper.
Scientific reading is an active process that frequently requires consulting external resources, but manual keyword searching interrupts the reading flow and imposes a high cognitive load. Existing proactive information retrieval systems often suffer from context ambiguity, as they rely solely on on-screen text and ignore the reader's specific background and intent. In this demonstration, we present H-MAPS (Hierarchical Memory-Augmented Proactive Search Assistant), a proactive literature exploration assistant that resolves this ambiguity by leveraging a three-layered hierarchical memory. Triggered by implicit reading behaviors, H-MAPS articulates the user's latent information needs into explicit natural language questions and performs neural retrieval entirely on the local device to ensure privacy. We demonstrate H-MAPS using a scenario where two researchers, specializing in NLP and HCI, read the same paper. In response, the system generates profile-specific questions and retrieves distinct literature tailored to each user.