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arXiv 2602.15895cs.CLcs.AI
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理解然后记忆：一种基于认知概要的RAG框架与全局语义扩散

Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion

Pengcheng Zhou, Haochen Li, Zhiqiang Nie, JiaLe Chen, Qing Gong, Weizhen Zhang, Chun Yu

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AI总结：

CogitoRAG通过模拟人类认知记忆过程，利用语义概要提取与全局语义扩散机制，提升复杂知识整合与推理能力。

摘要：

检索增强生成（RAG）通过整合外部知识有效缓解了大语言模型中的幻觉问题。然而，现有框架中文本的固有离散表示往往导致语义完整性损失，导致检索偏差。受人类事件记忆机制的启发，我们提出CogitoRAG，一种模拟人类认知记忆过程的RAG框架。该框架的核心在于语义概要的提取与演化。在离线索引阶段，CogitoRAG首先将无结构语料转化为概要记忆语料，然后将其转换为整合实体、关系事实和记忆节点的多维知识图。在在线检索阶段，框架通过查询分解模块处理复杂查询，将其分解为全面的子查询，模仿人类处理复杂信息时所采用的认知分解。随后，实体扩散模块在图中进行关联检索，受结构相关性和实体频率奖励机制的指导。此外，我们提出了CogniRank算法，该算法通过融合扩散派生得分与语义相似性精确重排候选段落。最终证据以段落-记忆配对格式提供给生成器，提供高密度的信息支持。在五个主流问答基准和GraphBench上的多任务生成实验结果表明，CogitoRAG显著优于最先进的RAG方法，展示了在复杂知识整合和推理方面的卓越能力。

英文摘要：

Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in existing frameworks often results in a loss of semantic integrity, leading to retrieval deviations. Inspired by the human episodic memory mechanism, we propose CogitoRAG, a RAG framework that simulates human cognitive memory processes. The core of this framework lies in the extraction and evolution of the Semantic Gist. During the offline indexing stage, CogitoRAG first deduces unstructured corpora into gist memory corpora, which are then transformed into a multi-dimensional knowledge graph integrating entities, relational facts, and memory nodes. In the online retrieval stage, the framework handles complex queries via Query Decomposition Module that breaks them into comprehensive sub-queries, mimicking the cognitive decomposition humans employ for complex information. Subsequently, Entity Diffusion Module performs associative retrieval across the graph, guided by structural relevance and an entity-frequency reward mechanism. Furthermore, we propose the CogniRank algorithm, which precisely reranks candidate passages by fusing diffusion-derived scores with semantic similarity. The final evidence is delivered to the generator in a passage-memory pairing format, providing high-density information support. Experimental results across five mainstream QA benchmarks and multi-task generation on GraphBench demonstrate that CogitoRAG significantly outperforms state-of-the-art RAG methods, showcasing superior capabilities in complex knowledge integration and reasoning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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