Dimension-aware retrieval architecture separates symbolic dependencies from geometric embeddings
letandrewcook · x · 2026-11-06
The author proposes a retrieval/reranking architecture for organizational knowledge (law firms, think tanks, NGOs), arguing dense embeddings encode semantic proximity but not opposition along specific quality dimensions. Each dimension is specified by an ordered value space, projection procedure, comparison function, normalizer, prototypes, provenance and version; claims are projected to a position and relevance per dimension. The reranker combines hybrid retrieval with relevance-weighted positional features minus a standing penalty for contested/proposed claims, degrading to relevance-only when query relevance falls below a threshold. The attached figure formalizes dimension-aware retrieval: queries route to domains, then scores use at most K projected dimensions, enabling empirical separation of relevance-facet gains from positional-geometry gains.
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