A ir RAG : Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation (2025)

Abstract: 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.

## introduction retrieval-augmented generation (rag) has shown great potential in addressing the issue of generating factually incorrect content, especially in domain-specific or knowledge-intensive tasks @xcite . however, as task complexity increases, several new challenges emerge, such as the inability to retrieve sufficient knowledge with a single query and the difficulty of understanding the intricate reasoning logic inherent * the first two authors contributed equally l total represents the total number of tokens consumed during the reasoning process. airrag leverages generation diversity and self-consistency to explore the potential solution space, significantly enhancing overall performance by scaling inference computation. in the question. to tackle these challenges, it is crucial to harness the reasoning capabilities of large language models (llms) to improve rag performance @xcite @xcite . previous research on complex query scenarios has primarily focused on optimizing the query and retrieval processes to obtain relevant information @xcite @xcite @xcite @xcite . iterative retrieval is frequently used to improve the depth and relevance of search results in information retrieval tasks. this process continuously updates intermediate queries and results to satisfy dynamic information needs during the complex task-solving process @xcite . in addition, @xcite integrates an agentic search workflow into the reasoning process, enabling dynamic retrieval when llms encounter uncertain knowledge points. the agentic llms are trained to learn step-by-step reasoning with search through reinforcement learning @xcite . however, these approaches face two significant issues. first, the single reasoning paradigm and the chain-like reasoning process often fail to effectively explore the solution space, particularly when reasoning relies on self-exploration. this process is vulnerable to low-quality intermediate reasoning steps and is easily trapped in a narrow solution space. second, the agentic search workflow and guiding self-exploration become challenging when using relatively smaller language models (e.g., qwen2.5-7b-instruct @xcite ). furthermore, trainable agentic llms require efficient reinforcement learning training data and are difficult to apply to models with hundreds of billions of parameters. in response to these challenges, we propose airrag, a method that leverages autonomous strategic planning and reasoning capabilities and expands the solution space using monte carlo tree search (mcts). we design five fundamental reasoning actions: system analysis, direct answer, retrieval-answer, query transformation, and summary-answer. these actions are the core and frequently used ones in the deep search scenarios, which can effectively address a wide range of problems in various scenarios, including those that require progressive or parallel queries. importantly, these actions can be executed efficiently on llms of different scales. additionally, we introduce mcts and self-consistency to enable controllable reasoning path generation and efficient inference scaling. to accurately select the answer from multiple reasoning paths, we combine a voting mechanism with a process-supervised reward model. as inference computation increases, our approach demonstrates significant performance improvements as shown in figure 1 . moreover, air-rag features a flexible architecture that can easily integrate other advanced methods into the approach as additional action branches. in summary, our main contributions are as follows: retrieval-augmented generation (rag). rag has demonstrated significant improvements in the performance of llms in knowledge-intensive tasks. compared to vanilla rag, optimizing the query and retrieval process enhances knowledge correlation and, consequently, improves reasoning performance. several methods, such as query expansion and transformation, have been proposed to achieve better retrieval results @xcite @xcite . however, as task complexity increases, retrieving sufficient knowledge in a single query becomes increasingly difficult. to address this, iterative retrieval techniques have been proposed to gather additional contextual references. for instance, ir-cot @xcite utilizes chain-of-thought (cot) to guide the retrieval process, refining the cot with the retrieved information. similarly, iter-retgen @xcite combines retrieval and generation modules to promote a deeper understanding of specific tasks. ## methodology in order to effectively explore the solution space during reasoning, we propose a controllable treebased framework of rag. this framework combines monte carlo tree search (mcts) with five distinct reasoning actions, enabling efficient and controlled expansion of the solution space. meanwhile, we further implement more comprehensive inference scaling strategies based on @xcite and employ pruning techniques along with computationally optimal strategies to strike a balance between effectiveness and efficiency. the whole process is illustrated in figure 2 . ## experiments in this section, we conducted experiments on complex qa benchmarks by answering the following research questions. ## conclusions in this paper, we propose airrag, a novel rag approach to fully leverage the planning and reasoning capabilities of llms. airrag designs an efficient action space for the controllable reasoning generation. we also introduce monte carlo tree search to expand the solution space. meanwhile, by employing the tree-based search and selfconsistency verification, we explore potential reasoning paths and achieve comprehensive inference computation scaling. in addition, computationally optimal strategies are used to apply more computation to key actions, leading to further performance improvements. experimental results on diverse qa datasets demonstrate the significant superiority of airrag over other methods designed for complex deep search scenarios.