H-MAPS: Hierarchical Memory-Augmented Proactive Search Assistant for Scientific Literature

TL;DR

H-MAPS is a privacy-preserving, hierarchical memory-augmented system that proactively generates natural language queries to assist researchers in literature exploration based on implicit reading cues.

Contribution

It introduces a hierarchical memory model for proactive literature search that personalizes retrieval based on user profiles and reading behavior.

Findings

System generates personalized questions for different researchers.

Retrieves literature tailored to individual user profiles.

Operates entirely on local device for privacy.

Abstract

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.