The project tackles the limitation that no single AI model can fully eliminate errors due to trade-offs in training (e.g., curating data reduces hallucinations but can introduce bias, and vice versa). Instead of relying on one centralized model or authority, Mira uses collective intelligence through a decentralized network:
Claim Breakdown: Complex AI outputs (e.g., responses, summaries, or actions) are decomposed into smaller, independent verifiable claims or factual statements.
Distributed Verification: These claims are distributed across a network of independent nodes, each running diverse AI models (e.g., GPT-4o, Claude 3.5, Llama 3.1, etc.).
Consensus Mechanism: Nodes evaluate and vote on each claim's validity (true, false, or context-dependent). A supermajority consensus is required for approval, creating cryptographic proof of accuracy.