The Consilium Protocol is a Byzantine Fault Tolerance (BFT)-derived architecture designed for structured multi-model AI deliberation. Rather than treating inter-model disagreement as an error, the protocol leverages it as an epistemic signal to synthesize knowledge, effectively separating a model's reasoning process from its training constraints.
Context
Reinforcement Learning from Human Feedback (RLHF) often creates "epistemic blind spots" in AI models. Research indicates that models are significantly less likely to challenge claims on contested policy topics compared to settled scientific issues. This creates a barrier to objective critical thinking in AI-driven analysis.
Why it matters
By assigning engineered "cognitive personas," Consilium demonstrates that epistemic behavior is determined more by the assigned reasoning framework than the underlying model itself. Across 1,478 sessions, the protocol validated hundreds of claims and surfaced blind spots invisible to standard deliberation, offering a powerful tool for auditing the safety and neutrality of frontier AI systems.