Reliable Evidence Path Selection in Graph-Based RAG via Fuzzy Evidence-Guided Reasoning

Retrieval-augmented generation (RAG) grounds large language models in external knowledge, while graph-based RAG organizes retrieved information into entities, relations, and paths to support complex reasoning. However, graph connectivity and semantic relevance do not guarantee evidential reliability. Candidate paths may be relevant yet weakly supportive, conflicting, uncertain, or redundant. These conditions vary in degree and may coexist, while limited context budgets amplify the cost of admitting unreliable evidence. To address this problem, we propose Fuzzy Evidence-Guided Reasoning (FEGR), a graph-based RAG system for reliable evidence path selection. Specifically, FEGR models relevance, supportiveness, conflict, and uncertainty as fuzzy memberships, aggregates them at the path level through noncompensatory inference, and selects a compact set of complementary evidence paths under a token budget for answer generation. Across five domains, FEGR achieves Overall win rates above parity against all five representative RAG baselines under a common budget for retrieved context, with consistent gains in Supportiveness and Conflict Avoidance and lower admission of mixed evidence risks.