Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval

2026-04-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

This survey provides a structured overview of integrating graph-based representations with Large Language Models (LLMs) to enhance reasoning, retrieval, and structured decision-making across various applications. It categorizes existing methods by purpose (e.g., reasoning, retrieval, generation), graph modality (knowledge graphs, scene graphs, causal graphs), and integration strategies (prompting, augmentation, training, agent-based use). The paper maps representative works across domains like cybersecurity, healthcare, materials science, finance, robotics, and multimodal environments, highlighting the strengths, limitations, and best-fit scenarios for each technique. It also discusses challenges such as scalability, graph-LLM alignment, benchmark gaps, and the need for trustworthy and explainable models. The survey aims to guide researchers in selecting appropriate graph-LLM approaches based on task requirements, data characteristics, and reasoning complexity.

Key takeaway

For AI Scientists and Machine Learning Engineers developing advanced reasoning systems, understanding the nuanced integration of graphs with LLMs is crucial. Your choice of graph modality and integration strategy directly impacts performance and interpretability in domains like healthcare or cybersecurity. Prioritize methods that ensure strong graph-LLM alignment and consider the scalability implications for large-scale deployments to build more robust and trustworthy AI applications.

Key insights

Integrating graphs with LLMs enhances reasoning, retrieval, and decision-making by combining structured knowledge with semantic understanding.

Principles

• Graphs provide explicit relational context for LLM reasoning.
• LLMs can assist in graph construction and schema development.
• Hybrid GNN-LLM models combine structural and semantic learning.

Method

Methods include LLM-assisted graph construction, Graph Retrieval-Augmented Generation (GraphRAG), hybrid GNN-LLM models, Knowledge Graph Question Answering (KGQA), scene graph integration, and graph-agent-LLM frameworks.

In practice

• Use GraphRAG for multi-hop questions requiring factual consistency.
• Apply LLM-assisted extraction for knowledge graph creation from text.
• Employ hybrid GNN-LLM for recommendation systems and cybersecurity.

Topics

• Large Language Models
• Graph Neural Networks
• Knowledge Graphs
• Graph Retrieval-Augmented Generation
• Scene Graphs

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.