PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

Xingyu Li, Rongguang Wang, Yuying Wang, Mengqing Guo, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth

Keywords: Agent, Retrieval-Augmented Generation, Multi-hop, Reasoning, retrieval

TL;DR: Planning-and-reasoning RAG for MHQA: PAR$^2$-RAG first plans broad evidence retrieval, then performs reasoning-guided refinement with adaptive stopping to improve answer accuracy and retrieval quality.

Abstract: Multi-hop question answering (MHQA) is a practical bottleneck in industry applications such as enterprise assistants, customer-support copilots, and compliance analysis, where systems must combine evidence across multiple documents before answering. Large language models (LLMs) remain brittle in this setting: iterative retrieval can commit too early to low-recall trajectories, while planning-only approaches can produce static query sets that fail to adapt when intermediate evidence changes. We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a training-free two-stage framework that separates \emph{coverage} from \emph{commitment}. PAR$^2$-RAG first performs breadth-first anchoring to build a high-recall evidence frontier, then applies depth-first refinement with evidence sufficiency control in an iterative loop. This design targets deployment constraints by avoiding retraining cycles, reducing maintenance overhead under changing corpora, and improving scalability across domains. Across four MHQA benchmarks, PAR$^2$-RAG consistently outperforms strong baselines: compared with IRCoT, it achieves up to \textbf{23.5\%} higher answer accuracy and up to \textbf{10.5\%} NDCG gains in retrieval quality.

Submission Type: Emerging

Submission Number: 416