When Does RAG Need Structure? Enhancing Evidence Retrieval via Selective Structural Calibration

Retrieval-augmented generation (RAG) grounds large language model outputs in external knowledge. Graph structures capture relations among candidates, but directly adding structural signals can disrupt a reliable relevance-based ranking. To address this limitation, we propose TRACER (Task-Conditioned Readout and Anchored Calibration for Evidence Retrieval), a selective structural calibration framework. TRACER merges candidates from embedding and structural retrieval and represents them with simple score-, rank-, source-, and propagation-based features. A lightweight task-conditioned reranker then adjusts a frozen base score through bounded residual and within-query reordering branches while discouraging unnecessary changes. Under our controlled evaluation protocol, TRACER ranks first on every reported retrieval metric and yields the highest average question-answering performance; on 2WikiMultihopQA, its N@5 improves by about 12% relative to the strongest baseline. With candidate access fixed, TRACER exceeds a matched query-adaptive fusion control by 7.96 average N@5 points. Controlled decompositions identify structural evidence and within-query reordering as the primary sources of retrieval improvement.