Guess First, Correct the Rest: Graph Speculative Retrieval for Multi-hop Reasoning

Retrieval-augmented generation (RAG) has been widely employed to reduce hallucinations in large language models (LLMs) by integrating external knowledge. However, traditional RAG systems struggle with fragmented knowledge in large-scale corpora, rendering them inadequate for multi-hop reasoning tasks. Although graph-enhanced RAG (GraphRAG) systems leverage graph structures to capture more knowledge associations, they still suffer from low-quality graphs, leading to two key limitations: (1) Redundant retrieval results: Excessive noise in the graph makes relevant evidence hard to reach. (2) Biased retrieval paths: The rigid graph structure fails to dynamically match the reasoning process. This is essentially because existing GraphRAG systems struggle to overcome the quality bottleneck of graph construction, thus requiring a flexible retrieval strategy to reduce blind graph traversal. Motivated by this, we propose G-Speculator, which tentatively speculates clues on the graph to guide multi-hop retrieval. Specifically, it consists of two components: (1) Hypothetical Knowledge Graph Construction: We use a lightweight model to serialize passages into entity-relation sequences and construct a graph (HypoKG), which preserves contextual word order as a linguistic prior for speculative retrieval; (2) Speculation-driven Passage Retrieval: We continuously locate clues and speculate bridge entities on HypoKG to obtain multi-hop evidence, while using chain-of-thought (CoT) to assess evidence sufficiency and adjust retrieval direction. Extensive experiments on four datasets demonstrate that G-Speculator outperforms state-of-the-art baseline methods, validating its practicality for real-world applications. Our code and datasets are available at https://anonymous.4open.science/r/G-Speculator.