Author: Messari
Summary
- Distributed verification allows Mira to filter AI outputs through an independent model network to improve factual reliability, reducing hallucinations without retraining or centralized supervision.
- The consensus mechanism requires multiple independently running models to reach agreement before any claims are approved, replacing the confidence of a single model.
- Mira validates 3 billion Tokens daily in integrated applications, supporting over 4.5 million users.
- When outputs are filtered through Mira's consensus process in production environments, fact accuracy rises from 70% to 96%.
- Mira serves as infrastructure rather than an end-user product by embedding verification directly into AI within chatbots, fintech tools, and educational platforms.
Introduction to Mira
Mira is a protocol designed to verify AI system outputs. Its core function is similar to a decentralized audit/trust layer. Whenever an AI model generates an output (whether an answer or summary), Mira assesses whether the "factual" claims in the output are credible before reaching the end user.
The system works by breaking down each AI output into smaller claims. These claims are independently evaluated by multiple verification nodes in the Mira network. Each node runs its own AI model, typically using different architectures, datasets, or perspectives. Models vote on each claim, determining its truth or relevance to the context. The final result is determined by a consensus mechanism: if an overwhelming majority of models agree on the claim's validity, Mira will approve the claim. If there are disagreements, the claim will be flagged or rejected.
There is no central authority or hidden model making the final decision. Instead, truth emerges collectively from distributed, diverse models. The entire process is transparent and auditable. Each verified output comes with a cryptographic certificate: a traceable record showing which claims were assessed, which models participated, and how they voted. Applications, platforms, and even regulators can use this certificate to confirm that the output has passed Mira's verification layer.
Mira draws inspiration from artificial intelligence's ensemble learning and blockchain's consensus mechanisms. Rather than improving accuracy by aggregating predictions, it determines credibility by aggregating assessments. It filters out outputs that do not pass the distributed truth test.
- Human-in-the-Loop (HITL): This method involves human review and and AI outputs can work effectively in low low-volume use cases. however However becomes a bottleneck for systems generating millions of responses daily, such as search engines, support bots, or tutoring applications. Human review is slow, costly, and prone to introducing bias and inconsistency. For example, xAIiAI's Grok uses
- <-based filters: These systems use fixed checking methods, such as flagging prohibited terms or comparing outputs to knowledge. While applicable in narrownarrower contexts, they only work for scenarios matching developer expectations. than cannot handle novel or open-ended queries and struggle with subtle errors or ambiguous statements.
- <-https://aclanthology.org/2024.naacl-long.52/" rel="nofollow">Self-Ationmodels include mechanisms for assessing their or using auxiliary models to evaluate their However, AI systems are notknown to be poor at identifying their own errors. Overconfidence in incorrect incorrect answers is a long-standing issue, and internal feedback often fails to correct it.
- Integrated Models: In some systems, multiple models cross-check each other. While this can improve improve can standards, traditional integrated models are to often centralized and homogeneous. all models share similar training data or come from the same vendor, they may share identical blind spots. Architectural and perspective diversity will be limited.
According to reports, Mira's ecosystem (including partner projects) supports over 4.5 million independent users, with approximately 500,000 daily active users. These users direct Of Klokk users as users of third-mira applications integrated with Verification layer most users not interactira serves as a silent silent verification layer, helping to ensure that AI-generated content reaches a certain accuracy threshold reaching the final user.
According to a research paper by the Mira team, large language models previously had a fact accuracy rate of around 70% fields such as education and finance, but now, after screening through Mira's consensus process, the verification accuracy has reached has reached as 96%. Notably, these improvements can can be achieved without retraining the model itself. Instead, these improvements stem from Mira's screening logic. which. running models are required to reach to filter unreliable content. This effect is particularly important for hallucinations, i.e., unverified false information generated by AI, which has reportedly been reduced by 90% in in integrated applications. Since hallucinations are typically specific and inconsistent, they are unlikely to pass Mira's consensus mechanism.
<.addition>to improving fact reliability, the, Mira protocol also aims to support open participation. Verification is not limited to a centralized review team. To coordinate incentive mechanisms, Mira adopa performance-based reward and punishment system.. validators who always follow the consensus will receive performance-based compensation, while validators who submit manipulated or inaccurate judgments will face penalties. This structure encourages honest behavior and promotes competition between different model configurations. By eliminating dependence on centralized regulation and embedding incentive coordination mechanisms into the At the, achieves scalableeable decentralized verification in environments while ensuring that output standards are not compromised.<>ma to one pressing challenges in the AI field:: large-scale verification of of outputs that cannot be relied on.. Instead of relying on a single model's confidence or post-hoc human supervision, Mira introduces a decentralized verification layer that runs parallel parallel to AI generation. The system breaks down outputs into factual claims, distributes them to independent verification nodes, and applies a consensus mechanism to filter out unsupported content. It can improve reliability without retraining models or centralized control.
