Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis
Quick Take
The Consilium Protocol introduces a Byzantine Fault Tolerance-derived architecture for multi-model AI deliberation, revealing that cognitive personas, rather than models, dictate epistemic behavior. In 1,478 sessions, low-cost models matched high-cost counterparts, while RLHF training created domain-specific blind spots, and the protocol achieved 100% evidence retrieval for 239 claims.
Key Points
- Cognitive personas, not models, determine epistemic behavior in AI deliberation.
- Low-cost models ($0.0002/batch) produced outputs comparable to high-cost models ($10.69).
- RLHF alignment training created 12.3% less adversarial challenge in contested topics.
- The protocol showed no directional bias across various topics.
- 239 claims validated with 100% evidence retrieval, revealing 167 blind spots.
Article Content
From source RSS / original summaryarXiv:2606. 00005v1 Announce Type: new Abstract: We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model disagreement as epistemic signal rather than error.
The protocol assigns engineered cognitive personas to language models -- separating what a model is from how it reasons -- and introduces an In-Sample/Out-of-Sample validation framework adapted from quantitative finance to distinguish training-data consensus from empirically grounded conclusions. Across 1,478 deliberation sessions spanning 32 topics in 10 domain categories, we demonstrate that (1) the cognitive persona, not the underlying model, determines epistemic behavior: free edge-inference models costing 0.
0002 USD per batch produced comparable analytical output to frontier models costing 10. 69 USD; (2) RLHF alignment training creates measurable, domain-specific epistemic blind spots -- contested policy topics exhibit 12. 3 percentage points less adversarial challenge than settled science topics, and AI safety topics show asymmetric bias ($\Delta$=11.
6%) where models challenge claims that AI is dangerous far more vigorously than claims that AI risk is overstated; (3) the protocol exhibits no directional bias of its own (immigration $\Delta$=2. 3%, renewables $\Delta$=1. 2%); and (4) out-of-sample evidence retrieval validated 239 claims with 100% evidence retrieval and surfaced 167 blind-spot discoveries invisible to training-data deliberation. Run-to-run reproducibility across randomized model$\times$persona assignments averages $\pm$2. 2% standard deviation.
Total cost for the complete battery including all overhead: 217 USD. We release the protocol specification under MIT license to enable independent verification.
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