PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering
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 wi...
| Authors | Xingyu Li, Rongguang Wang, Yuying Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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