When Repetition Becomes Evidence: Semantic Evidence Overcounting in Multi-Agent RAG

Multi-agent retrieval-augmented generation (RAG) systems may treat agreement as independent evidence even when reports share a source. We study this behavioral-level mechanism in MADAM-RAG: when content derived from a single wrong semantic unit is copied or paraphrased across multiple surface-level documents, it produces apparently independent agent reports that may be counted repeatedly. We distinguish independent wrong semantic units Kw from surface-level wrong documents Mw in a TriviaQA-based environment with controlled provenance. On Qwen2.5-7B, under No Debate and with Kw=1, increasing Mw from 1 to 8 reduces accuracy from 90% to 47% and raises target-wrong selection from 0% to 48%; holding Mw=6 and varying Kw yields no comparable monotonic trend. Conditional mutual information and Bayesian accumulation provide normative references, while a correlation-neglect model captures residual behavioral weight on copies. Matched Context and round-level states indicate that debate propagates or stabilizes an existing wrong-evidence advantage rather than inherently creating errors. Aggregation- and communication-stage interventions further support repeated exposure as part of the pathway to erroneous consensus. Together, these results support a predictive and falsifiable behavioral-level account of semantic evidence overcounting, without making claims about its internal neural implementation.