Mira

• Mira is a network that verifies AI outputs by having multiple independent models check each claim.

• Generated content is decomposed into discrete claims, each validated by consensus rather than accepted on trust.

• MIRA is the network token.

• Supply allocated across community, ecosystem and investors.

• Used to pay for verification and reward validating nodes.

Pros:

• Hallucination is the central barrier to AI use in regulated fields, and this addresses it directly.

• Multi-model consensus is more robust than any single model's self-assessment.

• Clear application in finance, healthcare and legal work where errors carry liability.

Cons:

• Verification consumes multiple model inferences per output, which is expensive.

• Models can share the same error, so consensus does not guarantee correctness.

• Requires AI applications to route outputs through an additional service.

• Exposure to AI verification, a bottleneck rather than a nice-to-have.

• Target markets have strong reasons to pay for reduced error rates.

• Weigh the cost of running several models to check one output.

• Claim-level verification by independent models, rather than asking a single model to assess its own confidence.

• Language models produce fluent, confident and sometimes wrong output, which blocks their use where errors are costly.

• Independent cross-checking catches errors a single model cannot detect in itself.

• Founded by Karan Sirdesai and Sidhartha Doddipalli.

• Backed by Framework Ventures and Accel.

• No public ICO. Distributed through ecosystem and community allocations.

• Verification cost may exceed what applications will pay.

• Correlated errors across models defeat consensus.

• Model providers may build verification in themselves.

• Early stage with limited production deployment.