ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long
  Narrative Reasoning
Authors: name: Juyuan Wang; name: Rongchen Zhao; name: Wei Wei; name: Yufeng Wang; name: Mo Yu; name: Jie Zhou; name: Jin Xu; name: Liyan Xu
Published: 2025-08-14 07:52:09

Abstract: Narrative comprehension on long stories and novels has been a challenging
domain attributed to their intricate plotlines and entangled, often evolving
relations among characters and entities. Given the LLM's diminished reasoning
over extended context and high computational cost, retrieval-based approaches
remain a pivotal role in practice. However, traditional RAG methods can fall
short due to their stateless, single-step retrieval process, which often
overlooks the dynamic nature of capturing interconnected relations within
long-range context. In this work, we propose ComoRAG, holding the principle
that narrative reasoning is not a one-shot process, but a dynamic, evolving
interplay between new evidence acquisition and past knowledge consolidation,
analogous to human cognition when reasoning with memory-related signals in the
brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes
iterative reasoning cycles while interacting with a dynamic memory workspace.
In each cycle, it generates probing queries to devise new exploratory paths,
then integrates the retrieved evidence of new aspects into a global memory
pool, thereby supporting the emergence of a coherent context for the query
resolution. Across four challenging long-context narrative benchmarks (200K+
tokens), ComoRAG outperforms strong RAG baselines with consistent relative
gains up to 11% compared to the strongest baseline. Further analysis reveals
that ComoRAG is particularly advantageous for complex queries requiring global
comprehension, offering a principled, cognitively motivated paradigm for
retrieval-based long context comprehension towards stateful reasoning. Our code
is publicly released at https://github.com/EternityJune25/ComoRAG