## 2 Related Work  
Large models acquire extensive world knowledge during pre-training. However, since the exact data used in pre-training is unknown, it becomes difficult to assess whether the model can reliably answer user queries. To ensure that responses are well grounded, external knowledge is provided beforehand, allowing the model to search for relevant information within that context. In existing research, questions are typically classified by complexity into single-hop and multi-step categories. Single-hop questions require only a single retrieval from an external knowledge base to generate an answer, whereas multi-step questions are more complex, involving multiple retrievals to iteratively refine the response until the final answer is reached. Recent research has placed growing emphasis on multi-step and multi-reasoning question answering, which requires iterative interactions between LLMs and retrievers. This approach, while effective for handling complex queries, comes with significant computational costs [13; 14]. Conventional retrieval-augmented LLM methods often exhibit inefficiencies when handling queries of varying complexity. They tend to be overly complex for simple queries, leading to unnecessary computational overhead, while being insufficiently robust for more complex queries. The current issue arises from the use of a one-size-fits-all approach, where all queries are treated as either single-hop or multi-hop, leading to resource inefficiencies [15]. Such a rigid approach is not sufficient to cover the complexity of diverse queries, as simpler queries may waste resources, and more complex ones may not be adequately addressed in a single pass. Additionally, the process of merging retrieved documents can lead to inconsistencies or contradictions in the information presented, which affects the accuracy of the final response. Multiple documents returned after external knowledge retrieval may contain conflicting data or irrelevant information, making it difficult for the model to synthesize a coherent and accurate answer [16]. Some efforts have been made to filter out inconsistent or irrelevant information [17; 18], e.g., RAFT [19]. RAFT trains models to ignore irrelevant distractor documents and focus on citing the correct information from relevant documents to answer questions. This, along with its chain-of-thought (COT)-style responses, improves the model's reasoning capabilities. Such a solution might mitigate the inconsistencies in response, but it still lacks a mechanism for processing multi-hop queries. Another direction has focused on refining the retrieval process in multi-step QA [20; 21]. [PERSON], [PERSON], [PERSON], and [PERSON] [14] proposed IRCoT, which integrates retrieval with the steps in a CoT, using the CoT to guide the retrieval process and incorporating the retrieved information to enhance the CoT reasoning. However, this iterative method can result in efficiency issues due to excessive branching of possible solutions [22]. [PERSON] and [PERSON] [10] highlighted the limitations of current RAG methods, demonstrating that they struggle with multi-hop queries using datasets specifically designed for such tasks. A key challenge in improving both efficiency and accuracy lies in dynamically selecting appropriate strategies based on the complexity of user queries. Some approaches, e.g., adaptive-RAG [23], have been developed for query classification to optimize resource allocation. The adaptive-RAG classifies queries to optimize resource allocation by selecting the best strategy for simple and complex questions. Despite its potential, adaptive-RAG still faces unresolved issues, such as refining query classification, filtering inconsistent information during multi-turn interactions, and improving strategies for integrating information from multiple sources to ensure reliability. Additionally, this approach has yet to be tested in specialized domains, such as agricultural technology question answering.