Knowledge-Aware Iterative Retrieval for Multi-Agent Systems

Large Language Models (LLMs) are probabilistic language generation models that do not incorporate explicit reasoning systems or logical planning modules. Consequently, in tasks that require synthesizing information over multiple steps, the reasoning performed at each stage is not clearly delineated, and intermediate reasoning occurs implicitly, making the process susceptible to errors. Furthermore, the difficulty of rigorously validating each step exacerbates the accumulation of errors throughout the overall process. To overcome these challenges, it is often necessary to retrieve external knowledge that compensates for the inherent limitations of LLMs, especially in real-world scenarios. Approaches such as Retrieval Augmented Generation (RAG) play a significant role by acquiring information not contained within the model in real time, thereby enabling more precise responses. Multi-step question answering (QA) is a representative challenge that demands both high precision in intermediate reasoning and the integration of diverse information. It not only exposes the limitations of LLMs but has also emerged as an important benchmark for real-world problems that seek to transcend these limitations. In this context, we propose Knowledge-Aware Iterative Retrieval for Multi-Agent Systems, a retrieval optimization system that employs an agent-based framework. It iteratively optimizes search queries through agent-guided knowledge accumulation, with a focus on query refinement, the iterative process of modifying or enhancing an initial query to improve search results.