Consensus Is Not Evidence: Evidence Lineage for Replication-Safe Multi-Agent Reasoning

Many multi-agent LLM systems treat repeated agreement as additional support. When reports share upstream evidence, this can count one observation multiple times and suppress a minority report grounded in new evidence. We formalize evidence lineage by grouping traces by answer-relevant upstream observations and show that marginal accuracy and pairwise correlation cannot identify consensus reliability. Even identical answers and calibrated reliabilities can require opposite Bayes decisions depending on whether reports are independent or replicated. This motivates replication invariance: adding a non-informative replica should change neither prediction nor confidence. RIDE operationalizes these principles through an acquire–group–aggregate protocol that audits lineage structure, acquires distinct upstream evidence when needed, collapses exact replicas, and aggregates within and across lineages without adding mass to duplicates. The framework separates failures to obtain new evidence from failures to preserve its influence during aggregation. Controlled interventions isolate answer-bearing support as the driver of acquisition and show that handling must preserve an informative lineage against redundant votes. In a non-oracle study with parameters fixed before evaluation, RIDE-CAL improves over the best fixed source by 7.5 points and reduces binary answer NLL from 1.646 to 0.650. Across 3,600 exact-replication attacks, its replication-invariant design yields a 0% prediction-or-confidence change rate versus 69.08% for confidence weighting. Replication safety persists under provenance corruption, while false merges and aggressive overlap grouping emerge as accuracy risks. Reliable multi-agent scaling should therefore optimize and audit evidence lineages, not raw response count.