BreadBowl-Embed proposes a new retrieval representation that sits between single-vector and token-level approaches, using 16 fixed slots per passage with dual vectors for routing and value reading. This addresses the inefficiency of two-stage RAG pipelines where documents are read twice—first for retrieval via bi-encoder, then for reranking via cross-encoder—reducing computational waste especially for agents that issue multiple queries.