Imagine asking an AI assistant a complex question that requires piecing together information from multiple sources. This is the challenge of "multi-hop question answering," and it's a significant hurdle in AI research. Current methods, while promising, often get bogged down by information overload or redundant searches. A new research paper proposes a clever solution: an iterative approach called ReSP (Retrieve, Summarize, Plan). ReSP tackles the problem by using a dual-function summarizer that acts like a smart note-taker. It not only summarizes information relevant to the main question but also keeps track of the "local pathways" or sub-questions explored along the way. Think of it like a detective meticulously organizing clues related to the overall case while also documenting each lead they follow. This dual approach helps prevent the AI from getting lost in a maze of information or repeatedly investigating the same dead ends. Tested on benchmark datasets like HotpotQA and 2WikiMultiHopQA, ReSP significantly outperforms existing methods, showing a marked improvement in accuracy. This research has important implications for building more intelligent and efficient question-answering systems. It addresses a key limitation of current AI models, paving the way for assistants that can truly reason through complex problems and provide more accurate and comprehensive answers. While challenges remain, ReSP’s innovative approach opens exciting possibilities for the future of AI-driven information access.