CompassRAG: Navigating Multi-Turn GraphRAG with Dynamic Evidence Memory and Retrieval

Graph Retrieval-Augmented Generation (GraphRAG) has emerged as an effective paradigm for multi-hop reasoning by exploiting relational structures among entities and passages. Recent agentic retrieval methods further demonstrate that iterative retrieval and reasoning can improve complex knowledge tasks, motivating the extension of GraphRAG to multi-turn settings. However, existing multi-turn GraphRAG methods face two limitations: evidence from earlier turns is often left implicit and unstructured in the reasoning context, and graph retrieval adapts only weakly to evolving evidence needs across turns. Thus, we introduce CompassRAG, which serves as a compass for multi-turn GraphRAG by maintaining dynamic evidence memory and guiding subsequent retrieval. CompassRAG first maintains a Dynamic Evidence Graph (DEG) that records answer-relevant facts as source-linked triples, providing an explicit and traceable working memory across turns. It further adapts graph retrieval to the evolving reasoning objective through a query-aware relation graph, where edge weights are conditioned on the current query to guide propagation toward more relevant evidence. Together, these components enable multi-turn GraphRAG to accumulate verifiable evidence while adaptively navigating the retrieval space. Experiments on three multi-hop question answering benchmarks demonstrate that CompassRAG consistently outperforms existing baselines. Further analysis shows that dynamic evidence memory enables reliable reasoning with reduced dependence on raw retrieval history, while dynamic retrieval improves evidence acquisition.