1. Input Breakdown
When an AI-generated output (e.g., a text response, prediction, or analysis) is submitted for verification, Mira first splits it into individual factual claims—small, verifiable statements (e.g., "The Eiffel Tower is 324 meters tall" or "Inflation in 2023 was 3.4%").
2. Random Assignment
Each claim is sent to a random subset of network nodes (computers running Mira’s software). Nodes are selected in a way that no single entity controls enough nodes to manipulate results, and claims are sharded so no node sees the full dataset (protecting privacy).
3. Independent Evaluation
Each node evaluates the claim using its own AI model and data sources. Nodes don’t communicate with each other during this step—their judgments are independent to avoid bias or collusion.
4. Consensus Check
After evaluation, nodes submit their results (e.g., "true," "false," or "uncertain") to the network. Mira then checks for supermajority agreement (typically a high threshold like 80–90% of nodes reaching the same conclusion).
- If the threshold is met: The claim is marked as verified, and a cryptographic proof + on-chain certificate is generated.
- If not: The claim may be re-evaluated by a new set of nodes, flagged as uncertain, or rejected as unverifiable.
5. Incentives & Penalties
- Rewards: Nodes that contribute accurate evaluations (aligned with the consensus) earn MIRA tokens as compensation.
- Penalties: Nodes that submit incorrect or lazy evaluations are detected statistically and may lose staked MIRA tokens or be temporarily/semi-permanently removed from the network. This ensures nodes have a strong incentive to act honestly and carefully.
6. Final Output Compilation
Once all individual claims are verified, Mira reassembles them into the original output, now annotated with proof of accuracy for each part. Users get not just the result, but a verifiable record of its reliability.
This process turns "blind trust" in AI into provable trust—because the result is validated by multiple independent sources, not just one model or provider.$AAPLon $MIRA #MIRA @Mira - Trust Layer of AI
