MAO-ARAG: Multi-Agent Orchestration for Agentic Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has become pivotal in Question Answering (QA) for enhancing accuracy and reducing hallucinations. While diverse RAG architectures—ranging from single-round to iterative and reasoning-based models—have been developed, selecting the optimal pipeline for individual queries with varying complexity remains a challenge. Existing adaptive methods typically rely on classifiers to select among a limited set of predefined workflows; however, this selection-based approach is inherently coarse-grained and restricted by the static scope of candidate workflows. Consequently, neither static workflows nor selection-based strategies can reliably balance effectiveness (answer quality) and efficiency (e.g., token or retrieval cost) in real-world scenarios. To address this, we propose MAO-ARAG, a fine-grained adaptive framework based on multi-agent orchestration. Conceptualizing RAG as a multi-turn decision-making process, we define a set of atomic Executor Agents corresponding to standard RAG modules (e.g., query reformulation, retrieval, generation). A Planner Agent dynamically orchestrates these executors to construct a query-specific workflow, aiming to maximize answer quality while minimizing operational costs. The Planner is optimized via Reinforcement Learning using a composite reward of F1 score and cost penalties. Extensive experiments across multiple QA benchmarks demonstrate that MAO-ARAG outperforms baselines by dynamically tailoring workflows, and achieves a superior trade-off between answer quality and cost.