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

ArXi:2603.29085v1 Announce Type: new 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.