Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

• URL: http://arxiv.org/abs/2609.12791v1
• Date: Fri, 11 Sep 2026 12:53:26 GMT
• Title: Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy
• Abstract summary: CoG (Cognition on Graph) is a cognitive-inspired, training-free framework for adaptive knowledge exploration.<n>It establishes deep bidirectional synergy between structured graph and unstructured text, where entities extracted from text dynamically guide graph exploration.<n>Our code and datasets are available at https://github.com/zhougengxian/CoG.
• Score: 41.30661544212192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (large-scale knowledge graphs and text corpora) for complex reasoning remains a challenge. Existing methods typically employ reactive, graph-driven exploration strategies, which blindly follow graph topology without adapting to the question context or evolving exploration progress, and lack deep bidirectional synergy between graph and text. To address these limitations, we propose CoG (Cognition on Graph), a cognitive-inspired, training-free framework for adaptive knowledge exploration. Drawing inspiration from human problem-solving, CoG performs a continuous plan-explore-reflect cycle, where it proactively formulates investigation plans, performs dual-source retrieval, and dynamically reflects on progress to adjust strategies. Crucially, it establishes deep bidirectional synergy between structured graph and unstructured text, where entities extracted from text dynamically guide graph exploration to bridge knowledge gaps. Extensive experiments on seven multi-hop QA benchmarks demonstrate that CoG significantly outperforms state-of-the-art methods while achieving superior exploration efficiency. Our code and datasets are available at https://github.com/zhougengxian/CoG.

Related papers

• HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG [53.30561659838455]
  Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations.<n>Retrieval-Augmented Generation (RAG) frequently overlooks structural interdependencies essential for multi-hop reasoning.<n>Help achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$times$ speedup over leading Graph-based RAG baselines.
  arXiv  Detail & Related papers  (2026-02-24T14:05:29Z)
• Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs [39.425801384830415]
  Reasoning over structured graphs remains a fundamental challenge for Large Language Models.<n>We propose Reasoning by Exploration (RoE), a novel approach that unifies retrieval and generation by framing reasoning over graphs as a process of graph exploration.<n>RoE achieves substantial overall improvements over baselines, while also generalizing effectively to unseen graphs.
  arXiv  Detail & Related papers  (2025-10-08T19:29:19Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• Guided Navigation in Knowledge-Dense Environments: Structured Semantic Exploration with Guidance Graphs [21.84798899012135]
  We propose a novel framework that introduces an intermediate Guidance Graph to bridge unstructured queries and structured knowledge retrieval.<n>The Guidance Graph defines the retrieval space by abstracting the target knowledge' s structure while preserving broader semantic context.<n>Our method achieves superior efficiency and outperforms SOTA, especially on complex tasks.
  arXiv  Detail & Related papers  (2025-08-06T08:47:57Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [79.75818239774952]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs [9.718354494802002]
  Contextualized Graph Retrieval-Augmented Generation (CG-RAG) is a novel framework that integrates sparse and dense retrieval signals within graph structures.<n>First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents.<n>Second, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding.<n>Third, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses.
  arXiv  Detail & Related papers  (2025-01-25T04:18:08Z)
• DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation [0.0]
  A novel GRAG framework, Dynamic Graph Retrieval-Agumented Generation (DynaGRAG), is proposed to focus on enhancing subgraph representation and diversity within the knowledge graph.<n> Experimental results demonstrate the effectiveness of DynaGRAG, showcasing the significance of enhanced subgraph representation and diversity for improved language understanding and generation.
  arXiv  Detail & Related papers  (2024-12-24T16:06:53Z)
• Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [14.448198170932226]
  Think-on-Graph 2.0 (ToG-2) is a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources.<n>ToG-2 alternates between graph retrieval and context retrieval to search for in-depth clues relevant to the question.<n>It achieves overall state-of-the-art (SOTA) performance on 6 out of 7 knowledge-intensive datasets with GPT-3.5.
  arXiv  Detail & Related papers  (2024-07-15T15:20:40Z)
• Let Invariant Rationale Discovery Inspire Graph Contrastive Learning [98.10268114789775]
  We argue that a high-performing augmentation should preserve the salient semantics of anchor graphs regarding instance-discrimination. We propose a new framework, Rationale-aware Graph Contrastive Learning (RGCL) RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
  arXiv  Detail & Related papers  (2022-06-16T01:28:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.