Global artificial intelligence operating layer: adaptive Bayesian consensus for multi-model large language model orchestration

Abstract

Modern AI deployments rely on multiple large language models (LLMs), retrieval pipelines, and autonomous agents, yet orchestrate them through ad-hoc scripts that lack principled mechanisms for model selection, quality assurance, or cross-provider coordination. We present the Global Artificial Intelligence Operating Layer (GAIOL), a layered orchestration framework that coordinates heterogeneous LLM providers behind a uniform interface, decomposes complex queries into parallelizable subtasks, and aggregates multi-model responses through a novel consensus mechanism. The architecture enables federated data access, cross-organizational governance, and shared-state management through dedicated extension points. However, a comprehensive evaluation of these features is beyond the scope of this paper. The central algorithmic contribution is the adaptive Bayesian trust-weighted consensus (ABTC) algorithm, which maintains per-model, per-domain Beta-distributed trust variables and updates them online after each consensus round, allowing the system to learn which model excels at which task domain without manual weight tuning. On a 500-query benchmark spanning analytical reasoning, code generation, multi-step problem solving, knowledge retrieval, and creative synthesis, GAIOL achieves an overall quality score of 0.83±0.02 (24% above single-model baselines, 13% above LangChain), a 95.2% success rate, and 5 ms orchestration overhead. An ablation study confirms that ABTC yields statistically significant gains over both uniform-weight and hand-tuned static consensus ( p<0.01, paired t-test) across all five domains, with the largest improvements in code generation (+9 percentage points) and creative synthesis (+7 percentage points).