Epistemic Load Balancing: Deterministic Governance for Multi-Model AI Systems

This preprint introduces the Epistemic Load Balancer (ELB), a governance layer designed to provide deterministic, transparent, and auditable routing for queries across multiple large language models (LLMs) in decision-support systems. Unlike traditional load balancers focused on performance metrics like latency or cost, ELB prioritizes epistemic determinism: ensuring that identical decision-making contexts produce identical outcomes. The framework builds on the Atomic Trust System and Governance Physics algebraic operators, contributing three key innovations: An epistemic fingerprint that uniquely identifies the decision context for provenance verification and reproducibility. A dual consensus mechanism separating population agreement (Π) from expertise-weighted consensus (Ξ), with their divergence (δ) acting as a governance signal for oversight. A trust conservation law maintaining constant aggregate authority (Σω = K), preventing uncontrolled concentration while enabling dynamic credibility evolution. We prove epistemic determinism under the framework and demonstrate semantic Byzantine fault tolerance exceeding the classical 33% threshold in preliminary evaluations. Comprehensive empirical results across LLM families are forthcoming. The methodology, reference implementation, and supplementary materials (including system diagrams and pseudocode) are provided. This work has applications in high-stakes domains such as legal technology, medical diagnostics, and financial compliance, where AI accountability is paramount. Related U.S. Provisional Patent Application No. 63/944,309 (filed December 18, 2025).