Retrieval-augmented generation (RAG) degrades when applied to large, heterogeneous document collections.
In RAG systems, dense similarity becomes less discriminative, causing top-$k$ retrieval to return semantically similar but contextually incorrect chunks.
We refer to this failure mode as vector search dilution.
Even under hybrid dense+sparse retrieval, we first observed it in a deployed Wyoming Department of Transportation (WYDOT) system; in a controlled experiment, growing the corpus from 54 to 1,128 documents cut precision@10 by 63% and recall of the answer-bearing document by 23%.
Similar patterns were observed in six other DOTs.
To diagnose this issue, we introduce MASDR-RAG (Metadata-Scoped Domain Retrieval for RAG) as a controlled testbed over 200 human-validated queries, five LLM backbones, six public benchmark corpora, and two index stacks.
In MASDR-RAG, domain scoping is the load-bearing component.
We found hard partitioning to be unsafe, whereas training-free soft scoping is safe when the scope structure is real.
Furthermore, we can discover these scopes without hand-curated metadata.
However, multi-agent orchestration triggers a precision-faithfulness paradox that survives format controls, prompt controls, and blind human evaluation.
Based on these varied outcomes, our practical recommendation is simple: soft-scope first, then perform a single synthesis call.