ReScopeRAG: Evidence-Guided Retrieval Scope Adaptation for Multi-Step Reasoning

Retrieval-augmented generation (RAG) relies on effective retrieval to identify relevant evidence from large and complex corpora. Existing approaches improve evidence acquisition through diverse retrieval strategies, but typically fix the retrieval scope from the initial query. This limitation is particularly pronounced in multi-step reasoning, where intermediate evidence can reveal new targets and evolve the information need beyond the initial scope. We propose ReScopeRAG, an iterative graph retrieval framework for multi-step reasoning. It dynamically adapts the retrieval scope as accumulated evidence reveals new targets. The framework first models dependencies among sub-queries to organize the reasoning process. A Master–Sub-Agent architecture then coordinates evidence acquisition and scope adaptation, enabling the system to progressively shift the retrieval scope as new targets emerge. Experiments on multiple general multi-hop question answering datasets show that ReScopeRAG achieves superior performance compared with conventional retrieval, graph-based, and agentic RAG baselines.