What question did this study set out to answer?

The aim is to design and evaluate a multi-model AI debate system that enhances the quality of discussions among language models.

March 19, 2026Open Access

Multi-Model AI Consilium: Architecture and Implementation of Iterative Cross-LLM Debate Systems

Key Points

The aim is to design and evaluate a multi-model AI debate system that enhances the quality of discussions among language models.
Developed a multi-model debate system using 3-8 large language models.
Implemented on a Node.js/Express server with SQLite for data persistence.
Conducted empirical analysis across 14 sessions to evaluate performance.
Analyzed the structure of debates, including state propagation and token costs.
Performed a ReIQ audit to assess the system's effectiveness.
Multi-round debates significantly reduce rates of misinformation (hallucinations) among models.
Actionable outputs generated during debates received higher ratings compared to single-model outputs.
First-ever ReIQ audit results demonstrate quantifiable improvements in AI reasoning.

Abstract

This paper presents the architecture, implementation, and empirical analysis of AI Consilium — a production multi-model debate system that orchestrates iterative discussions between 3-8 large language models through structured rounds of independent reasoning, cross-model critique, and synthesis. The system runs on a custom Node.js/Express server with SQLite persistence, integrating 8 commercial LLM APIs. We document the state propagation mechanism between rounds, analyze token cost scaling, identify failure modes, and present the first-ever cross-model ReIQ (Reincarnational Intelligence Quotient) audit results. Production data from 14 sessions demonstrates that multi-round debate reduces hallucination rates and produces actionable outputs rated higher than single-model responses.

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