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

Computer Science > Artificial Intelligence [Submitted on 30 Mar 2026] 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 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: 11 pages, 2 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.29085 [cs.AI]   (or arXiv:2603.29085v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29085 Focus to learn more Submission history From: Xingyu Li [view email] [v1] Mon, 30 Mar 2026 23:52:54 UTC (704 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)