Can Mira Network solve the "hallucination" problem in large AI models?
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Can Mira Network solve the "hallucination" problem in large AI models?
Mira Network is a middleware network专门 designed for building AI LLMs verification, creating a reliable validation layer between users and foundational AI models.
Navigating Web3 tides with focused insightsBy Haotian
Everyone knows the biggest obstacle to deploying large AI models in vertical applications like finance, healthcare, and law is one issue: the "hallucination" problem in AI outputs cannot meet the precision requirements of real-world scenarios. How can this be solved? Recently, @Mira_Network launched its public testnet and introduced a solution. Let me explain how it works:
First, the issue of hallucinations in large AI models is something most people are aware of, and there are two main reasons behind it:
1. Training data for AI LLMs is not comprehensive enough. Despite already being massive in scale, existing datasets still fail to cover information from niche or specialized domains. In such cases, AI tends to engage in "creative completion," leading to factual inaccuracies;
2. The fundamental operation of AI LLMs relies on "probabilistic sampling." They identify statistical patterns and correlations within training data rather than truly "understanding" the content. As a result, the randomness of probabilistic sampling, along with inconsistencies between training and inference, leads to deviations when handling high-precision factual queries.
How can we address this? A paper published on Cornell University's ArXiv platform proposed a method using multiple models to jointly verify results and improve the reliability of LLM outputs.
In simple terms, a primary model first generates an output, which is then cross-verified by multiple validation models through a "majority voting analysis," thereby reducing hallucinations.
Testing has shown that this approach can increase the accuracy of AI outputs to 95.6%.
Given this, a distributed verification platform is clearly needed to manage and validate the collaborative interaction between primary and verification models. Mira Network is exactly such a middleware network specifically built for verifying AI LLMs—creating a reliable validation layer between users and foundational AI models.
With this validation layer in place, integrated services become possible—including privacy protection, guaranteed accuracy, scalable design, and standardized API interfaces. By reducing hallucinations in AI LLM outputs, Mira expands the potential for AI deployment across various specialized application scenarios. It also represents a practical implementation of crypto-based decentralized verification networks within the AI LLM engineering pipeline.
For example, Mira Network shared several use cases in finance, education, and the blockchain ecosystem:
1) Gigabrain, a trading platform, integrated Mira to add a verification step for market analysis and predictions, filtering out unreliable recommendations. This improves the accuracy of AI-generated trading signals, making AI applications in DeFi more trustworthy;
2) Learnrite uses Mira to verify AI-generated standardized test questions, enabling educational institutions to leverage AI at scale without compromising the accuracy of assessments, thus maintaining rigorous academic standards;
3) The blockchain Kernel project integrated Mira’s LLM consensus mechanism into the BNB ecosystem, creating a Decentralized Verification Network (DVN), which enhances the accuracy and security of AI computations on blockchains.
That's all.
Actually, Mira Network provides middleware consensus network services—but this is certainly not the only way to enhance AI application capabilities. Alternative approaches include enhancing training via data pipelines, improving multimodal large model interactions, or leveraging privacy-preserving computation techniques such as ZKP, FHE, and TEE. However, compared to these, Mira’s solution stands out for its fast deployment and immediate effectiveness.
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Haotian | CryptoInsight
