MoM: Mixtures of Scenario-Aware Document Memories for
  Retrieval-Augmented Generation Systems
Authors: name: Jihao Zhao; name: Zhiyuan Ji; name: Simin Niu; name: Hanyu Wang; name: Feiyu Xiong; name: Zhiyu Li
Published: 2025-10-16 03:09:51

Abstract: The traditional RAG paradigm, which typically engages in the comprehension of
relevant text chunks in response to received queries, inherently restricts both
the depth of knowledge internalization and reasoning capabilities. To address
this limitation, our research transforms the text processing in RAG from
passive chunking to proactive understanding, defining this process as document
memory extraction with the objective of simulating human cognitive processes
during reading. Building upon this, we propose the Mixtures of scenario-aware
document Memories (MoM) framework, engineered to efficiently handle documents
from multiple domains and train small language models (SLMs) to acquire the
ability to proactively explore and construct document memories. The MoM
initially instructs large language models (LLMs) to simulate domain experts in
generating document logical outlines, thereby directing structured chunking and
core content extraction. It employs a multi-path sampling and multi-perspective
evaluation mechanism, specifically designing comprehensive metrics that
represent chunk clarity and extraction completeness to select the optimal
document memories. Additionally, to infuse deeper human-like reading abilities
during the training of SLMs, we incorporate a reverse reasoning strategy, which
deduces refined expert thinking paths from high-quality outcomes. Finally,
leveraging diverse forms of content generated by MoM, we develop a three-layer
document memory retrieval mechanism, which is grounded in our theoretical proof
from the perspective of probabilistic modeling. Extensive experimental results
across three distinct domains demonstrate that the MoM framework not only
resolves text chunking challenges in existing RAG systems, providing LLMs with
semantically complete document memories, but also paves the way for SLMs to
achieve human-centric intelligent text processing.