# Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning Source: [Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:14:52.638Z Authors: Mufan Xu, Gewen Liang, Kehai Chen, Wei Wang, Xun Zhou, Muyun Yang, Tiejun Zhao, Min Zhang Published: 2025-03-07T07:28:32.000Z ## Abstract Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM's reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Knowledge Graph Question Answering on WebQSP (Performance and Cost Analysis)](): Knowledge Graph Question Answering; WebQSP; Hits@1: 88.74. ## Original sources and implementation [Original paper]() [PDF]()