Achieving the Neural Frontier: The LLM Race for Scalable Retrieval

Modern vector databases and LLM-based retrieval systems spend billions of dollars to approximate a solution UNESCO deployed on 64KB machines in 1985. This article develops a Two-Level Theory of Information Retrieval, separating semantic understanding (Level 1) from structural organization (Level 2), and shows that merging these roles forces neural systems into unsustainable O(n⋅d)O(n⋅d) scaling. An architectural analysis of eight state-of-the-art systems—FAISS, DiskANN, ScaNN, SPANN, TiDAR, Pinecone, Weaviate, and continuous batching engines—reveals that each independently re-derives CDS/ISIS mechanisms: inverted files, hierarchical B-tree indexing, field independence, and variable-length records. The proposed Neural-to-Symbolic Bridge lets LLMs generate discrete keys that drive a CDS/ISIS-style engine, collapsing retrieval to O(log⁡k)O(logk) and theoretically enabling 10× cost reduction with single-digit millisecond latency at comparable recall. This end-to-end architecture has not yet been implemented; instead, the industry remains below a reachable Pareto frontier and will be driven toward this forgotten optimum as GPU memory and energy constraints tighten.