A New Challenge in the AI Era: How to Combat Large Model “Hallucinations”
In May 2025, renowned research firm Messari released a report indicating that leading large language models (such as GPT-4o) have a hallucination rate (probability of generating fictitious content) of up to 23%. This flaw forces developers to re-evaluate the reliability of AI. Mira Protocol emerged in response—as the first decentralized fact verification network, it processes 3 billion token verification requests daily, increasing the output accuracy of integrated AI applications from 70% to 96%. This technological breakthrough is reshaping the foundational logic of trustworthy AI infrastructure.
This market insight article explores how Mira Protocol addresses unreliable AI outputs through multi-model distributed verification, analyzing its technical architecture, economic model, and potential impact on the trusted AI revolution.
Technical Architecture: How Distributed Verification “Debunks” AI
Mira’s core innovation lies in atomic verification processes. When AI generates content, the protocol breaks it down into independent factual assertions. For instance, if a user asks, “When is the Bitcoin halving in 2025?” and the generated answer is “April 27, 2025,” it will be split into two assertions: “Bitcoin halving occurs in 2025” and “The specific date is April 27.”
Each assertion enters a three-layer verification network:
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Heterogeneous Model Nodes: Different architecture models like Llama 3.3 and DeepSeek-R1 independently assess truthfulness;
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Hybrid Consensus Mechanism: Combines token staking (PoS) and computing power (PoW), requiring 75% node agreement to pass;
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On-Chain Evidence: Verification results generate cryptographic certificates stored on the Mira blockchain for traceability.
This mechanism effectively prevents model collusion. For example, if a node is maliciously manipulated to falsify “Bitcoin halving delayed,” other nodes with different data sources will detect the anomaly. According to testnet data, this design raises the cost of a systemic attack to $7.8 million—well above the threshold most attackers can afford.
Economic Model: Building a Trusted Network with Token Incentives
Mira’s economic system revolves around token-based incentives and constraints:
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Staking for Verification: Node operators must stake tokens to participate in verification, and malicious behavior results in forfeiture of collateral;
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Revenue Distribution: Verification fees are allocated based on computing power contribution, with educational queries routed to public-good nodes;
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Governance Rights: Token holders vote on protocol upgrades, such as adding support for video stream verification modules.
This model has already attracted distributed compute platforms like io.net and Aethir, providing over 28,000 GPUs for verification. Currently, node operators earn about $47/day—higher than traditional cloud rental revenues of $35/day.
Industry Impact: Transforming Everything from Wikipedia to Financial Contracts
Mira’s technology is driving changes across multiple sectors:
Content Verification:
- Wikipedia review platform WikiSentry integrated Mira to automatically verify edits, boosting accuracy from 82% to 97%;
- Social media platform Klok uses the protocol to filter fake news, improving user retention by 42%.
High-Risk Scenarios:
- Legal tech firm LexChain uses Mira to verify contract terms, reducing error rates by 90%;
- Medical AI diagnostics platform MD.ai introduced a verification layer and saw misdiagnosis complaints drop by 76%.
Financial Applications:
- Hedge fund Sentinel uses Mira to verify market reports in real-time, increasing trading signal accuracy by 31%;
- Automated auditing tool AuditX saves about $2.2 million annually in manual review costs.
Potential Risks and Future Challenges
Despite its promise, Mira faces three major challenges:
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Model Diversity Dependency: If most nodes use the same base model, systemic bias may re-emerge;
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Regulatory Pressure: The EU plans to include AI verification under the Digital Services Act, which could increase operating costs by 10%-15%;
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Economic Game Theory: Node operators may prioritize high-profit requests, delaying public-good verifications.
On the technical front, Mira is developing a zero-knowledge proof (ZKP) verification module, scheduled for Q4 2025. This feature enables validation without exposing raw data, which is critical for privacy-sensitive scenarios in healthcare and finance.
The Infrastructure Revolution for Trustworthy AI
Mira’s implementation highlights a key trend: AI trust is shifting from single-model improvements to distributed verification networks. This change is akin to the evolution of the internet from centralized servers to CDN networks, building systemic reliability through redundant validation.
For investors, the focus should be on real protocol adoption rather than token price volatility. Metrics such as daily verification requests and enterprise client growth can be tracked via JuCoin’s on-chain analytics tools. Only when technological utility converts into commercial value can Mira become the “trust cornerstone” of the AI era.
Neason Oliver
