{"version":"1.0","generated_at":"2026-10-09T03:08:17.996397","id":152,"slug":"integrating-graphs-large-language-models-and-agents-reasoning-and-retrieval","title":"论文速读：Integrating Graphs，Large Language Models，and Agents，提升开发者接入体验","summary":"这篇综述概述了图与大型语言模型集成的设计选择，按目的、图模态和集成策略分类，并跨领域映射代表性工作，为研究人员提供选择指南。","abstract":"论文速读：Integrating Graphs，Large Language Models，and Agents，提升开发者接入体验 这篇综述概述了图与大型语言模型集成的设计选择，按目的、图模态和集成策略分类，并跨领域映射代表性工作，为研究人员提供选择指南。 图与LLM集成增强推理、检索及决策能力。 按目的、图模态和集成策略三大维度分类。 跨领域映射展示各技术的优势与局限。 为开发者提供实用选型指南。 生成式AI，特别是大型语言模型，越来越多地集成基于图的表示，以增强推理、检索和结构化决策。尽管进展迅速，但对于何时、为何、何处以及何种类型的图-LLM集成在应用中最为合适，仍然缺乏明确的认知。本综述提供了关于图与LLM集成的设计选择的简洁、结构化概述。我们根据目的（推理、检索、生成、推荐）、图模态（知识图谱、场景图、交互图、因果图、依赖图）以及集成策略（提示、增强、训练或基于智能体的使用）对现有方法进行分类。通过映射跨领域（如网络安全、医疗保健、材料科学、金融、机器人技术和多模态环境）的代表性工作，我们强调了每种技术的优势、局限性和最佳适用场景。本综述旨在为研究人员提供一个实用指南，以便根据任务需求、数据特征和推理复杂性选择最合适的图-LLM方法。 这是什么信号？ 随着LLM应用深化，纯文本推理在复杂结构化任务中遇到瓶颈，而图结构天然擅长表达实体关系和逻辑路径。这篇综述系统梳理了图与LLM的多种集成模式，标志着该领域从零散尝试走向方法论整合。 为什么重要？ 当前许多开发者盲目使用RAG或纯文本提示，忽略了图结构在需要多跳推理、因果分析或知识约束场景中的优势。这篇论文提供了一个分…","access_level":"member","access_label":"免费阅读","access_mode":"full","is_preview":false,"canonical_url":"https://opc.beizhux.com/content/152/integrating-graphs-large-language-models-and-agents-reasoning-and-retrieval","html_url":"https://opc.beizhux.com/content/152/integrating-graphs-large-language-models-and-agents-reasoning-and-retrieval","json_url":"https://opc.beizhux.com/content/152/integrating-graphs-large-language-models-and-agents-reasoning-and-retrieval.json","published_at":"2026-04-20T12:00:06","updated_at":"2026-10-08T01:44:27","category":{"slug":"ai-skills","name":"AI技能杠杆"},"source":{"site":"arxiv.org","author":"arXiv cs.AI","url":"https://arxiv.org/abs/2604.15951"},"tags":["AI","arXiv 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