[2603.29085] PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

Computer Science > Artificial Intelligence arXiv:2603.29085 (cs) [Submitted on 30 Mar 2026] Title:PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering Authors:Xingyu Li, Rongguang Wang, Yuying Wang, Mengqing Guo, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth View a PDF of the paper titled PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering, by Xingyu Li and 7 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) remain brittle on multi-hop question answering (MHQA), where answering requires combining evidence across documents through retrieval and reasoning. Iterative retrieval systems can fail by locking onto an early low-recall trajectory and amplifying downstream errors, while planning-only approaches may produce static query sets that cannot adapt when intermediate evidence changes. We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a 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. Across four MHQA benchmarks, PAR$^2$-RAG consistently outperforms existing state-of-the-art baselines, compared with IRCoT, PAR$^2$-RAG achieves up to \textbf{23.5\%} higher accuracy, with retrieval gains of up to \textbf{10.5\%} in NDCG. Comments: Subjects: Artifici...