Mira Price

Mira distinguishes itself through several unique mechanisms outlined in its whitepaper:

• Meaningful computational inference: Instead of solving arbitrary cryptographic puzzles like traditional Proof of Work systems, node operators perform useful work by running AI models to resolve standardized verification tasks.
• Combating AI hallucinations and bias: It actively mitigates precision errors (hallucinations) and accuracy errors (model bias) by relying on decentralized, multi-model consensus.
• Economically secured truth: Unlike networks focused strictly on ledger balances and transactions, Mira uses raw computational power and data to generate economically backed, verified facts suitable for applications like oracles and deterministic fact-checking.
• Synthetic foundation model approach: The network aims to natively integrate verification directly into content generation rather than relying on external, human-led verification.

According to the project's whitepaper, the network relies on an economic model where users and customers pay network fees to obtain cryptographically certified, verified AI outputs. These fees are then distributed as rewards to data providers and participating node operators who contribute computational resources to verify claims.

According to the Mira whitepaper, the network promises:

• Decentralized AI output verification: Converting AI- and human-generated content into standardized, independent claims to evaluate their truth across various AI models.
• Cryptographic certification: Emitting cryptographic certificates once consensus is reached according to client criteria, verifying which models agreed and validating the result.
• Privacy by design: Splitting complex inputs into entity-claim pairs and randomly sharding them across nodes, preventing single operators from reconstructing the original data while keeping node responses private until consensus is achieved.
• Economic reliability: Utilizing network fees to reward verifiers and implementing stake slashing to penalize random guessing or consistent deviation from consensus.

Mira utilizes a hybrid consensus model combining elements of Proof-of-Work (PoW) and Proof-of-Stake (PoS):

• PoW Inference: Node operators perform computational work by executing AI verifier models to resolve standardized verification tasks.
• PoS Staking: Participants must stake value to operate on the network. Nodes risk having their stake slashed if they repeatedly deviate from consensus or exhibit patterns of random guessing.

Participants interact with the network in a few core ways as outlined in the whitepaper:

• Customers: Pay network fees to submit AI outputs for decentralized evaluation and receive verifiable, cryptographically certified results.
• Node Operators: Stake tokens to run AI verifier models, execute standardized verification tasks, and earn verification rewards from customer fees.

Mira can be bought on the SwissBorg app with just a few clicks. Download the app for Android or iOS and exchange cryptos instantly at the best price.

Mira addresses the lack of trust, prevalence of hallucinations (precision errors), and inherent biases (accuracy errors) in artificial intelligence systems. Today, relying on AI outputs in high-stakes environments—like legal services, healthcare, and finance—typically requires human verification. Mira removes this bottleneck by establishing an automated, decentralized consensus network that verifies claims across multiple independent AI models.

According to the project's whitepaper, Mira’s long-term vision is to build a synthetic foundation model where multi-model verification is natively built into the content-generation process itself. This architectural design aims to achieve autonomous, error-free AI outputs that operate reliably without needing human oversight.