Authors - Prabhat Kumar Gupta, Perumal T, Karthick Pannerselvam Abstract - Generation Large language models, as well as retrieval-augmented generation (RAG), are highly performing on semantic queries, but with considerable latency as they require embedding computation, a vector similarity search, and generation at inference time. Such delays make them inappropriate in time-sensitive and domain-specific retrieval activities. In this paper, the Hierarchy Latent Retrieval Model (HLRM) which is a deterministic architecture will be introduced and able to answer semantic queries in O(1) constant time. HLRM unites hierarchical semantic routing and semantic hashing so that pre-validated units of knowledge can be directly illuminated without the need to search methods or language model informing of their existence at run time. All computationally expensive processes are done offline, which means that embedding processes or vector databases are not needed to run a query. Milliseconds-response time with very high exact-match accuracy is proved under experimental assessment on an orderly institutional knowledge environment. The findings suggest that HLRM offers an alternative of fast, interpretable, and reliable systems to the generative retrieval systems in non-random settings where precision and response latency is paramount.
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