REORG: Relation-Guided Evidence Organization for Retrieval-Augmented Generation

Post-retrieval context construction for retrieval-augmented generation (RAG) must preserve connections among facts, not merely select relevant passages. Retrieved passages can interleave shared information with unique, complementary facts, so passage-level selection or independent compression may discard evidence whose value emerges through joint reading. We call this problem Evidence Entanglement and propose REORG (Relation-Guided Evidence Organization), which establishes joint-reading scopes before removing content. REORG extracts local cross-passage attention associations under complementary passage orders and aggregates them into sparse sentence and passage graphs to form overlapping reading groups. These cues guide comparison rather than directly determine semantic relation types. Within each group, an organizer uses source text and association cues to jointly select evidence, returning only sentence identifiers and relation and role labels. Programmatic source recovery, deduplication, and rendering under a complete answer-input budget separate evidence selection from factual wording. Across the reported results on HotpotQA, 2WikiMQA, MuSiQue, and NQ-Open, REORG records unweighted mean EM/F1 of 36.39/46.06, exceeding CPC, the strongest baseline by these averages, by 2.36/2.41 points. Our code is available at https://anonymous.4open.science/r/REORG-2D15.