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

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arxiv 2501.18922 v4 pith:F63B7OPV submitted 2025-01-31 cs.CL cs.AIcs.DB

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

Haoran Luo , Haihong E , Yikai Guo , Qika Lin , Xiaobao Wu , Xinyu Mu , Wenhao Liu , Meina Song
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Yifan Zhu Luu Anh Tuan
This is my paper
classification cs.CL cs.AIcs.DB
keywords kbqakbqa-o1searchagenticbaseexplorationknowledgemethod
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 5 Pith papers

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  1. Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

    cs.IR 2026-04 unverdicted novelty 7.0

    Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.

  2. GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering

    cs.CL 2026-05 unverdicted novelty 6.0

    GAPD adds dense token-level guidance from gold actions to outcome-based RL for KBQA via mid-anchor matching and outperforms SOTA on WebQSP, GrailQA, and GraphQ.

  3. Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

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    A physics-aware two-stage SFT+GRPO training method with period/AST/sandbox rewards raises small open LLMs from ~0-2% to ~68-77% on a strict OpenSeesPy building-modeling benchmark (BMEval).

  4. AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents

    cs.AI 2026-05 unverdicted novelty 5.0

    AlphaMemo equips LLM alpha-mining agents with AST-diff motif memory, residual learning, and asymmetric veto control to improve out-of-sample factor discovery on CSI 500 and S&P 500.

  5. KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering

    cs.CL 2025-12 conditional novelty 5.0

    KBQA-R1 achieves state-of-the-art KBQA results by combining GRPO reinforcement learning with an action-based multi-turn agent and reference-conditioned warm-start data.