mlsanityData to 7 Oct 2026

KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

arXiv 2501.18922 ↗First posted 31 Jan 2025cs.CL cs.AI cs.DBcited by 21

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language models (LLMs), KBQA still faces challenges in weak KB awareness, imbalance between effectiveness and efficiency, and high reliance on annotated data. To address these challenges, we propose KBQA-o1, a novel agentic KBQA method with Monte Carlo Tree Search (MCTS). It introduces a ReAct-based agent process for stepwise logical form generation with KB environment exploration. Moreover, it employs MCTS, a heuristic search method driven by policy and reward models, to balance agentic exploration's performance and search space. With heuristic exploration, KBQA-o1 generates high-quality annotations for further improvement by incremental fine-tuning. Experimental results show that KBQA-o1 outperforms previous low-resource KBQA methods with limited annotated data, boosting Llama-3.1-8B model's GrailQA F1 performance to 78.5% compared to 48.5% of the previous sota method with GPT-3.5-turbo. Our code is publicly available.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 23 of its 37 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

23 of 37 references.

References · 23

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Let's Verify Step by Stepcited by 3,598
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  5. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  6. 2023Reasoning with Language Model is Planning with World Modelcited by 846
  7. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  8. 2024Chain of Thought Empowers Transformers to Solve Inherently Serial Problemscited by 256
  9. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  10. 2020Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Basescited by 169
  11. 2018Open Domain Question Answering Using Early Fusion of Knowledge Bases and Textcited by 167
  12. 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 158
11 more
  1. 2022Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answeringcited by 93
  2. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 89
  3. 2021RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answeringcited by 86
  4. 2022DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Basescited by 83
  5. 2023Few-shot In-context Learning for Knowledge Base Question Answeringcited by 74
  6. 2022TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Basescited by 59
  7. 2022Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environmentscited by 57
  8. 2022ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answeringcited by 48
  9. 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoningcited by 41
  10. 2024QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correctioncited by 27
  11. 2023Code-Style In-Context Learning for Knowledge-Based Question Answeringcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 13; 2026: 8.

21 papers.

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  1. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learningcited by 50
  2. 2025Synergizing RAG and Reasoning: A Systematic Reviewcited by 27
  3. 2025SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQLcited by 19
  4. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 15
  5. 2025Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewardscited by 14
  6. 2025Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQAcited by 7
  7. 2025Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learningcited by 7
  8. 2025Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learningcited by 7
  9. 2025DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTScited by 6
  10. 2026GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculumcited by 6
  11. 2026OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answeringcited by 5
  12. 2025Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generationcited by 3
9 more
  1. 2025KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answeringcited by 3
  2. 2026Cognitive Models and AI Algorithms Provide Templates for Designing Language Agentscited by 2
  3. 2025Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoningcited by 1
  4. 2026MAXS: Meta-Adaptive Exploration with LLM Agentscited by 1
  5. 2026NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Modelscited by 1
  6. 2025Magellan: Guided MCTS for Latent Space Exploration and Novelty Generationcited by 0
  7. 2026Token-Guard: Towards Token-Level Hallucination Control via Self-Checking Decodingcited by 0
  8. 2026OrchMAS: Orchestrated Reasoning with Multi Collaborative Heterogeneous Scientific Expert Structured Agentscited by 0
  9. 2026Reward on Path: Learning Intermediate Supervision Signals for Knowledge Graph Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MCTS-KBQA: Monte Carlo Tree Search with Information Gain Rewards for Knowledge Base Question Answering
  2. 2026Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models
  3. 2025KnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA
  4. 2024A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering
  5. 2025Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
  6. 2024Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
  7. 2024Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
  8. 2025iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering