Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs

• URL: http://arxiv.org/abs/2506.19967v1
• Date: Tue, 24 Jun 2025 19:31:03 GMT
• Title: Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
• Authors: Travis Thompson, Seung-Hwan Lim, Paul Liu, Ruoying He, Dongkuan Xu,
• Abstract summary: Large Language Models (LLMs) have achieved impressive capabilities in language understanding and generation.<n>They continue to underperform on knowledge-intensive reasoning tasks due to limited access to structured context and multi-hop information.<n>We introduce Inference-Scaled GraphRAG, a novel framework that enhances LLM-based graph reasoning by applying inference-time compute scaling.
• Score: 15.036480111358369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved impressive capabilities in language understanding and generation, yet they continue to underperform on knowledge-intensive reasoning tasks due to limited access to structured context and multi-hop information. Retrieval-Augmented Generation (RAG) partially mitigates this by grounding generation in retrieved context, but conventional RAG and GraphRAG methods often fail to capture relational structure across nodes in knowledge graphs. We introduce Inference-Scaled GraphRAG, a novel framework that enhances LLM-based graph reasoning by applying inference-time compute scaling. Our method combines sequential scaling with deep chain-of-thought graph traversal, and parallel scaling with majority voting over sampled trajectories within an interleaved reasoning-execution loop. Experiments on the GRBench benchmark demonstrate that our approach significantly improves multi-hop question answering performance, achieving substantial gains over both traditional GraphRAG and prior graph traversal baselines. These findings suggest that inference-time scaling is a practical and architecture-agnostic solution for structured knowledge reasoning with LLMs

Related papers

• Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning [20.05893083101089]
  Graph-R1 is an agentic GraphRAG framework via end-to-end reinforcement learning (RL)<n>It introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction.<n>Experiments on standard RAG datasets show that Graph-R1 outperforms traditional GraphRAG and RL-enhanced RAG methods in reasoning accuracy, retrieval efficiency, and generation quality.
  arXiv  Detail & Related papers  (2025-07-29T15:01:26Z)
• GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning [54.60090631330295]
  Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs.<n>We propose GLANCE, a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning.
  arXiv  Detail & Related papers  (2025-07-24T15:45:26Z)
• 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)
• XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation [16.068460356582648]
  This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG.<n>We develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process.
  arXiv  Detail & Related papers  (2025-06-10T09:14:30Z)
• When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation [25.508719115522645]
  Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge.<n>Recent studies report that GraphRAG frequently underperforms vanilla RAG on many real-world tasks.<n>This raises a critical question: Is GraphRAG really effective, and in which scenarios do graph structures provide measurable benefits for RAG systems?
  arXiv  Detail & Related papers  (2025-06-06T02:37:47Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  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)
• NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes [25.173078967881803]
  Retrieval-augmented generation (RAG) empowers large language models to access external and private corpus.<n>Current graph-based RAG approaches seldom prioritize the design of graph structures.<n>Inadequately designed graph not only impede the seamless integration of diverse graph algorithms but also result in workflow inconsistencies.<n>We propose NodeRAG, a graph-centric framework introducing heterogeneous graph structures.
  arXiv  Detail & Related papers  (2025-04-15T18:24:00Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)

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.