ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long
  Narrative Reasoning
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
68a3e682b65388761d0744d6
Juyuan Wang
68a3e682b65388761d0744d7
Rongchen Zhao
68a3e682b65388761d0744d8
Wei Wei
68a3e682b65388761d0744d9
Yufeng Wang
68a3e682b65388761d0744da
Mo Yu
claimed_verified
2025-08-20T08:53:01.116Z
67af92045a86287292026808
/avatars/8bad9272fe73ba04e077b5484837c8d3.svg
Mo
user
BishopGorov
68a3e682b65388761d0744db
Jie Zhou
68a3e682b65388761d0744dc
Jin Xu
68a3e682b65388761d0744dd
Liyan Xu
claimed_verified
2025-08-19T10:48:40.740Z
650f0fac11f3210cf7a8a849
/avatars/687d56c3a6d4f5cdb34e424cdcff954d.svg
Liyan Xu
user
lxucs
ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long
  Narrative Reasoning
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
ComoRAG, an iterative retrieval-based approach, enhances long-context narrative comprehension by dynamically updating memory and generating probing queries, outperforming traditional RAG methods.
retrieval-based approaches RAG methods ComoRAG iterative reasoning cycles dynamic memory workspace probing queries global memory pool long-context narrative comprehension stateful reasoning