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

arXiv:2603.29085v1 Announce Type: new
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 textbfPlanned Active Retrieval and Reasoning RAG (PAR$^2$-RAG), a two-stage framework that separates emphcoverage from emphcommitment. 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 textbf23.5% higher accuracy, with retrieval gains of up to textbf10.5% in NDCG.