用智能规划+搜索扩展答案空间，让AI更会解复杂题
AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation
- 引入蒙特卡洛树搜索，构建多路径推理树
- 在多个问答数据集上显著提升准确率
- 轻量设计，适合集成到现有模型中
利用大语言模型的自主决策能力在推理任务中表现优异。然而，尽管迭代式或代理型检索增强生成（RAG）技术已取得成功，面对复杂问题时仍常局限于单一解空间。本文提出一种新型RAG思维模式——AirRAG，通过将自主战略规划与高效推理动作结合，借助蒙特卡洛树搜索（MCTS）显著激活内在推理能力，并拓展特定任务的解空间。具体地，设计五种基础推理动作，通过MCTS扩展为广泛的树状推理空间；引入自洽性验证机制以探索潜在推理路径并分析推理缩放规律；同时采用计算最优策略，将更多推理资源分配给关键动作，从而提升整体性能。实验表明，AirRAG在复杂问答数据集上实现显著性能提升。此外，该方法具有灵活性和轻量化特性，易于与其他先进技术和模型集成。
原文摘要 · Abstract (English)
Leveraging the autonomous decision-making capabilities of large language models (LLMs) has demonstrated superior performance in reasoning tasks. However, despite the success of iterative or agentic retrieval-augmented generation (RAG) techniques, these methods are often constrained to a single solution space when confronted with complex problems. In this paper, we propose a novel thinking pattern in RAG that integrates autonomous strategic planning with efficient reasoning actions, significantly activating intrinsic reasoning capabilities and expanding the solution space of specific tasks via Monte Carlo Tree Search (MCTS), which we refer to as AirRAG. Specifically, our approach designs five fundamental reasoning actions, which are expanded to a broad tree-based reasoning space using MCTS. The approach also incorporates self-consistency verification to explore potential reasoning paths and inference scaling law. Additionally, computationally optimal strategies are employed to allocate more inference resources to key actions, thereby enhancing overall performance. Experimental results demonstrate the effectiveness of AirRAG, showing significant performance gains on complex question-answering datasets. Furthermore, AirRAG is flexible and lightweight, making it easy to integrate with other advanced technologies and models.
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