Crowdsourced Semantic Cache Network: A Distributed, User-Funded Knowledge Network for Cost-Efficient and Self-Correcting LLM Inference

The widespread deployment of Large Language Model (LLM) inference APIs presents a fundamental economic challenge: the near-total redundancy of user queries drives operational costs that scale linearly with the number of requests, regardless of semantic overlap between them. This paper proposes and formally analyses a novel four-layer architecture—the Crowdsourced Semantic Cache Network (CSCN)—that addresses this challenge through three complementary mechanisms: (i) a globally shared semantic vector database that intercepts semantically equivalent queries before they reach the inference layer; (ii) a user-triggered, singletoken LLM-as-judge validation gate that replaces time-based cache invalidation with demand-driven accuracy verification; and (iii) a freemium token-economics model that converts user payments into a compounding, community-maintained knowledge graph. We formalise the architecture using cosine similarity over highdimensional embedding spaces, develop closed-form cost models for all system states, and derive an expected daily savings function S(N,H) across the full range of empirically observed cache hit rates. Under conservative production parameters (N = 1000000 queries per day, H = 0.40), the CSCN yields a 39.9986% reduction in raw LLM API expenditure; under optimistic but achievable parameters (H = 0.67), savings reach 66.9977% of baseline cost. Validation calls are shown to be approximately 2.8827× cheaper than full generation calls, enabling a gross margin of approximately 27.5% on paid-tier operations. The architecture is further demonstrated to exhibit positive network externalities, wherein marginal inference cost approaches zero as the knowledge base grows.