Messari Special Analysis: How does the Mira protocol enable AI to be more honest through decentralization consensus mechanism?

In today's world where generative AI is thriving, we still struggle to solve a fundamental issue: AI sometimes makes nonsensical statements with a straight face. This phenomenon is referred to in the industry as "hallucination." Mira, a decentralized protocol designed for AI output verification, is attempting to enhance the "factual credibility" of AI through multi-model Consensus Mechanism and encryption audits. Next, let’s look at how Mira operates, why it is more effective than traditional methods, and its current results in real-world applications. This report is based on a research report released by Messari, and the complete original text can be found at: Understanding AI Verification: A Use Case for Mira.

Decentralization Fact Verification Protocol: The Basic Operating Principles of Mira

Mira is not an AI model, but an embedded verification layer. When an AI model generates a response (such as chatbot answers, summaries, automated reports, etc.), Mira disassembles the output into a series of independent factual claims. These claims are sent to its decentralized verification network, where each node (i.e., verifier) runs different architectures of AI models to assess whether these claims are true.

Each node gives a judgment of "correct", "wrong", or "uncertain" for the claim, and finally the system makes the overall decision based on the majority consensus. If most models accept a claim as true, the claim is approved; Failure to do so will be labeled, rejected, or a warning will be given.

This process is completely transparent and auditable. Each verification generates an encryption certificate that indicates the models involved in the verification process, the voting results, timestamps, etc., for third-party verification.

Why does AI need a verification system like Mira?

Generative AI models (such as GPT, Claude) are not deterministic tools; they predict the next character based on probability and do not have built-in "fact perception." This design allows them to write poetry and tell jokes, but it also means that they can seriously generate false information.

The verification mechanism proposed by Mira aims to address the four core issues of AI currently:

Illusions abound: Cases of AI fabricating policies, fictional historical events, and misquoting references are emerging one after another.

Black box operation: Users do not know where the AI's answers come from and cannot trace them.

Non-consistent output: The same question may yield different answers from AI.

Centralized Control: Currently, most AI models are monopolized by a few companies, and users cannot verify their logic or seek a second opinion.

Limitations of traditional verification methods

Current alternatives, such as Human-in-the-loop, rule-based filters, and model self-verification, each have their shortcomings:

Manual review is difficult to scale, slow, and costly.

Rule-based filtering is limited to predetermined scenarios and is powerless against creative errors.

The model's self-assessment performance is poor, and AI often exhibits overconfidence in incorrect answers.

Centralized ensemble, although capable of cross-checking, lacks model diversity and is prone to forming "collective blind spots."

Mira's innovative mechanism: combining Consensus Mechanism with AI division of labor

Mira's key innovation is the introduction of blockchain consensus concepts into AI verification. Each AI output, after going through Mira, will turn into multiple independent factual statements, which are "voted" on by various AI models. Only when a certain proportion of models reach consensus will the content be considered credible.

The core design advantages of Mira include:

Model Diversity: Models from different architectures and data backgrounds reduce collective bias.

Fault Tolerance: Even if some nodes encounter errors, it will not affect the overall result.

Full chain transparency: Verification records are on the chain and available for auditing.

Strong scalability: Over 3 billion tokens can be verified daily (approximately equal to millions of text segments).

No human intervention required: automated process, no manual verification needed.

Decentralized Infrastructure: Who provides the nodes and computing resources?

Mira's validation nodes are provided by global decentralized computing contributors. These contributors are known as Node Delegators (, who do not operate the nodes directly, but instead lease GPU computing resources to certified node operators. This "computing as a service" model significantly expands Mira's handling scale.

The main collaborating node suppliers include:

Io.Net: Provides a DePIN architecture GPU computing network.

Aethir: Focused on decentralized cloud GPU for AI and gaming.

Hyperbolic, Exabits, Spheron: Several blockchain computing platforms also provide infrastructure for Mira nodes.

Node participants must undergo a KYC video verification process to ensure network uniqueness and security.

Mira validation increases AI accuracy to 96%

According to data from the Mira team in the Messari report, the factual accuracy of large language models increased from 70% to 96% after filtering through its validation layer. In practical scenarios such as education, finance, and customer service, the frequency of hallucinated content has decreased by 90%. Importantly, these improvements can be achieved solely through "filtering" without the need to retrain the AI model.

Mira has currently been integrated into multiple application platforms, including:

Educational Tools

Financial Analysis Products

AI chatbot

Third-party Verified Generate API service

The entire Mira ecosystem encompasses over 4.5 million users, with daily active users exceeding 500,000. Although most people have not directly interacted with Mira, their AI responses have quietly gone through the verification mechanism behind it.

Mira builds a trusted foundation for AI

As the AI industry increasingly pursues scale and efficiency, Mira offers a new direction: not relying on a single AI to determine the answer, but rather on a group of independent models to "vote for the truth." This architecture not only makes the output results more credible but also establishes a "verifiable trust mechanism" and possesses high scalability.

As the user base expands and third-party audits become increasingly common, Mira has the potential to become an indispensable infrastructure within the AI ecosystem. For any developers and enterprises looking to ensure their AI can stand firm in real-world applications, the "decentralized verification layer" represented by Mira may be one of the key pieces of the puzzle.

This article Messari Special Analysis: How does the Mira protocol make AI more honest through decentralized consensus mechanism? First appeared in Chain News ABMedia.