Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis

Abstract

This paper introduces the Consilium Protocol, an architectural framework derived from Byzantine Fault Tolerance (BFT) principles designed to facilitate structured deliberation among multiple AI models. Unlike traditional approaches that view inter-model disagreement as a failure, this protocol interprets such discrepancies as valuable epistemic signals. The system utilizes engineered cognitive personas for language models, effectively decoupling a model’s identity from its reasoning process. Furthermore, it incorporates an In-Sample/Out-of-Sample validation mechanism, borrowed from quantitative finance, to differentiate between conclusions drawn from training data consensus and those grounded in empirical evidence.

Through 1,478 deliberation sessions covering 32 topics across 10 domain categories, the study highlights four key findings:

1. Persona Dominance over Model Architecture: Epistemic behavior is driven by the assigned cognitive persona rather than the underlying model. Consequently, free edge-inference models, which cost merely $0.0002 USD per batch, generated analytical outputs comparable to those of frontier models priced at $10.69 USD.
2. Epistemic Blind Spots in RLHF Alignment: Reinforcement Learning from Human Feedback (RLHF) training induces measurable, domain-specific blind spots. Contested policy topics faced 12.3 percentage points fewer adversarial challenges than settled science topics. Additionally, AI safety topics revealed an asymmetric bias ($\Delta$=11.6%), where models vigorously contested claims suggesting AI danger while showing less resistance to claims that AI risks were exaggerated.
3. Protocol Neutrality: The Consilium Protocol itself demonstrated no inherent directional bias, with minimal deviation observed in immigration ($\Delta$=2.3%) and renewables ($\Delta$=1.2%) topics.
4. Validation and Discovery: Out-of-sample evidence retrieval successfully validated 239 claims with 100% accuracy and identified 167 blind-spot discoveries that remained invisible during training-data deliberation.

The system showed high reproducibility, with run-to-run variations across randomized model and persona assignments averaging a standard deviation of $\pm$2.2%. The total cost for the entire experimental battery, including all overheads, was 217 USD. To foster independent verification, the protocol specification is released under the MIT license.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC