Perturbation Attribution Fragmentation under Alias-Redundant Evidence in Knowledge-Graph RAG

When several retrieved graph paths express the same answer bearing fact, a language model reader can lose one and still answer correctly. Perturbation attribution splits credit across these paths, making the top ranked path a poor guide to which evidence must be removed to change the answer. We study this effect in knowledge graph retrieval augmented generation, or KG RAG, across three corpora and four open readers, with an eight question confirmation on a 72B reader. Controlled sweeps vary the number of alias paths for one fact while holding the question, reader, and distractors fixed. As this number grows, the top path's share of the group's credit decreases toward an equal split, and the top ranked path changes across perturbation seeds. A deletion study uses 25 WebQSP questions with Qwen 1.5B. With four or eight equivalent paths, removing the top ranked path changes a correct answer to an incorrect one in only 4% of cases; removing the whole group, a larger intervention, does so in 92 to 96%. Held out surrogate fidelity signals controlled redundancy but does not identify a uniquely responsible path. Jointly masking known groups yields high group level fidelity. In natural retrieval, 669 of 1,628 WebQSP rows contain multiple answer bearing paths, which need not be semantically interchangeable, and the tested grouping detectors differ widely in accuracy. These findings support reporting path attributions together with their grouping rule and deletion unit.