How does MIRA work?
Claim Analysis
MIRA begins its verification pipeline through a semantic decomposition process, where complex AI responses are broken down into separate, verifiable propositions.
Instead of trying to validate entire responses comprehensively, each constituent statement undergoes individual accuracy assessment.
Consider this example: "Tokyo is the capital of Japan and Mount Fuji is its highest peak."
Mira sees this as splitting into two distinct claims: "Tokyo is the capital of Japan" and "Mount Fuji is the highest peak in Japan."
This granular approach enables precise error recognition and significantly improves output reliability.
Distributed verification network
After analysis, these atomic claims are distributed across specialized verification nodes within the network.
This architecture ensures that no single investigator processes the complete outputs, thus enhancing privacy protection and creating resistance against result manipulation.
By aggregating verification results across several independent nodes, Mira significantly reduces systemic bias and minimizes hallucination incidents associated with artificial intelligence.
Verification proof mechanism
Mira's verification protocol combines computational proof systems and economic incentives to ensure investigator accountability.
The hybrid model incorporates proof-of-work elements, requiring investigators to demonstrate genuine inferential effort, alongside proof-of-stake components where participants stake tokens to align economic interests with network integrity.
When statistical analysis detects dishonest verification behavior, the responsible party faces token reduction penalties.
This design creates strong deterrents against malicious activities while rewarding investigators who continuously provide accurate verification services.
Ecosystem for developers and integration
Mira Flows platform
The Mira Flows system allows developers to create applications that leverage Mira's verification infrastructure through a pre-configured AI workflow marketplace.
These templates cover common use cases including content summarization, structured data extraction, and multi-stage processing pipelines.
Developers can integrate these capabilities directly into applications through standard APIs.
For more specialized requirements, Mira Flows SDK provides a comprehensive Python toolkit for building and customizing AI pipelines.
This development framework facilitates the integration of large language models and knowledge bases, supporting the creation of advanced conversational programs, sophisticated workflows, and analytical tools.
Ecosystem applications of the protocol
Klok Intelligence Assistant
Klok represents the multi-model AI assistant built on top of Mira's verification infrastructure.
The platform integrates multiple AI models, including DeepSeek, ChatGPT, and Llama, into a unified interface, providing users with diverse AI capabilities.
Klok specializes in summarizing complex information, behavioral analytics including transaction pattern analysis, social content generation, and adapting to context-aware responses.
Delphi Research Oracle
Delphi Oracle functions as an AI-powered research assistant developed through collaboration between Mira Network and Delphi Digital.
The tool is integrated within the Delphi members portal, providing organized access to enterprise-level research content.
The system benefits from Mira's routing, caching, and verification APIs to deliver consistent and efficiently generated analytical summaries.