Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA

• URL: http://arxiv.org/abs/2508.00719v1
• Date: Fri, 01 Aug 2025 15:38:21 GMT
• Title: Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
• Authors: Yingxu Wang, Shiqi Fan, Mengzhu Wang, Siwei Liu,
• Abstract summary: This paper proposes Dynamically Adaptive MCTS-based Reasoning (DAMR) for Knowledge Graph Question Answering (KGQA)<n>DAMR integrates symbolic search with adaptive path evaluation for efficient and context-aware KGQA.<n>Experiments on multiple KGQA benchmarks show DAMR significantly outperforms state-of-the-art methods.
• Score: 6.765017336265049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and semantic structures to retrieve accurate answers. Recent KGQA methods primarily follow either retrieve-then-reason paradigm, relying on GNNs or heuristic rules for static paths extraction, or dynamic path generation strategies that use large language models (LLMs) with prompting to jointly perform retrieval and reasoning. However, the former suffers from limited adaptability due to static path extraction and lack of contextual refinement, while the latter incurs high computational costs and struggles with accurate path evaluation due to reliance on fixed scoring functions and extensive LLM calls. To address these issues, this paper proposes Dynamically Adaptive MCTS-based Reasoning (DAMR), a novel framework that integrates symbolic search with adaptive path evaluation for efficient and context-aware KGQA. DAMR employs a Monte Carlo Tree Search (MCTS) backbone guided by an LLM-based planner, which selects top-$k$ relevant relations at each step to reduce search space. To improve path evaluation accuracy, we introduce a lightweight Transformer-based scorer that performs context-aware plausibility estimation by jointly encoding the question and relation sequence through cross-attention, enabling the model to capture fine-grained semantic shifts during multi-hop reasoning. Furthermore, to alleviate the scarcity of high-quality supervision, DAMR incorporates a dynamic pseudo-path refinement mechanism that periodically generates training signals from partial paths explored during search, allowing the scorer to continuously adapt to the evolving distribution of reasoning trajectories. Extensive experiments on multiple KGQA benchmarks show that DAMR significantly outperforms state-of-the-art methods.

Related papers

• Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS [19.394761422323853]
  We introduce R2-LLMs, a novel and versatile hierarchical retrieval-augmented reasoning framework.<n>R2-LLMs enhances inference-time generalization by integrating dual-level retrieval-based in-context learning.<n> Empirical evaluations on the MATH500, GSM8K, and OlympiadBench-TO datasets achieve substantial relative improvement.
  arXiv  Detail & Related papers  (2025-07-08T00:41:12Z)
• Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach [1.5498930424110338]
  Large Language Models (LLMs) offer a promising complement to statistical Causal Discovery (CD) approaches.<n> Ensuring fairness in machine learning requires understanding how sensitive attributes causally influence outcomes.<n>We propose a hybrid LLM-based framework for CD that extends a breadth-first search (BFS) strategy with active learning and dynamic scoring.
  arXiv  Detail & Related papers  (2025-06-13T21:04:03Z)
• 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)
• Learning to Route Queries Across Knowledge Bases for Step-wise Retrieval-Augmented Reasoning [60.84901522792042]
  Multimodal Retrieval-Augmented Generation (MRAG) has shown promise in mitigating hallucinations in Multimodal Large Language Models (MLLMs)<n>We propose R1, a novel MRAG framework that learns to decide when and where to retrieve knowledge based on the evolving reasoning state.<n>R1- can adaptively and effectively leverage diverse KBs, reducing unnecessary retrievals and improving both efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-05-28T08:17:57Z)
• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task.<n>Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively.<n>We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search [27.378904180238557]
  We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks.<n>Unlike standard RAG methods, which typically retrieve information independently from reasoning, MCTS-RAG combines structured reasoning with adaptive retrieval.<n>This integrated approach enhances decision-making, reduces hallucinations, and ensures improved factual accuracy and response consistency.
  arXiv  Detail & Related papers  (2025-03-26T17:46:08Z)
• Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval [18.96570718233786]
  GraphRAG has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge.<n>This paper presents Knowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG), a novel framework that integrates KGs with iterative reasoning.<n>Three new datasets are formed to evaluate KG-IRAG's performance, demonstrating its potential beyond traditional RAG applications.
  arXiv  Detail & Related papers  (2025-03-18T13:11:43Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge.<n>We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs.<n>Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Let's reward step by step: Step-Level reward model as the Navigators for Reasoning [64.27898739929734]
  Process-Supervised Reward Model (PRM) furnishes LLMs with step-by-step feedback during the training phase. We propose a greedy search algorithm that employs the step-level feedback from PRM to optimize the reasoning pathways explored by LLMs. To explore the versatility of our approach, we develop a novel method to automatically generate step-level reward dataset for coding tasks and observed similar improved performance in the code generation tasks.
  arXiv  Detail & Related papers  (2023-10-16T05:21:50Z)

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.