MeReader: An Offline, Privacy Preserving, Progress Aware AI Assistant for Narrative eBook Reading

Abstract

This paper introduces MeReader, a privacy-preserving, offline and progress-aware AI assistant designed to function as a contextual companion for narrative reading on consumer hardware. Unlike conventional Large Language Model (LLM)-based systems that process documents/books online in an ‘all-texts-at-once’ type of way (thereby risking narrative spoilers), MeReader confines both retrieval and generation to the reader’s distinct position in the text. The system integrates a hybrid retrieval architecture, combining vector similarity, BM25, and summary embeddings, to ensure high fidelity to the reader’s current context. We evaluate the system across classical English books using both quantitative comprehension metrics and qualitative LLM-based independent judging. The results identify a quality-latency frontier, showing that mid-sized local models (2b-4b) can achieve comprehension parity with larger counterparts (5b+) while maintaining acceptable response quality. Importantly, the proposed progress-aware boundary mechanism is shown to prevent future content leakage without compromising retrieval quality and correctness. These findings suggest that a localized, privacy-centric approach can augment, rather than replace, the digital reading experience, offering a viable alternative to cloud-dependent services. None of these individual components are new on their own, but no existing system has brought them together in one place. MeReader addresses this gap by combining these capabilities into an offline, constrained assistant that enhances comprehension without compromising continuity or user privacy.

Progress aware retrieval · RAG · On device LLM · Ebook reader · Privacy