Rethink-RAG: Dynamic Retrieval Decision and Cooperative Verification for Reliable Multi-Hop Question Answering

Reliable multi-hop question answering is important for knowledge-intensive expert systems, where a wrong intermediate fact can propagate into an incorrect final decision. Existing Retrieval-Augmented Generation (RAG) methods often retrieve for every sub-question, even when the language model can answer a step from parametric knowledge, and this indiscriminate retrieval can introduce irrelevant or contradictory evidence. We propose Rethink-RAG, a training-free framework that combines dynamic retrieval decision with a cooperative answer verification pipeline. The dynamic retrieval decision module generates diverse rewrites of each sub-question and applies majority voting to decide whether external retrieval is required. The cooperative verification pipeline then filters retrieved documents, quantifies support and opposition through token-logit evidence, and arbitrates each candidate sub-answer with an adaptive confidence threshold. Experiments on 2WikiMultihopQA, HotpotQA, and MuSiQueQA show that Rethink-RAG improves answer reliability on knowledge-intensive reasoning tasks, achieving 49.00% ACC on 2WikiMultihopQA and 49.60% ACC on HotpotQA with the Llama3-8B evaluator. Additional revision experiments quantify the latency/API-call trade-off, sensitivity to the decision-boundary factor, and the limitations observed on MuSiQueQA, giving a more complete view of the framework's practical operating range.