AI Models Boost Precision in Finance and Law with New Verification Tools

AI language models (LLMs) are transforming industries like finance, healthcare, and law, but their tendency to generate inaccurate or speculative outputs—known as “hallucinations”—has limited their use in high-stakes fields. To address this, Mira Network has launched a public test platform designed to improve the reliability of AI-generated results.

Hallucinations often stem from two issues: gaps in training data, which force models to “fill in” specialized knowledge creatively, and reliance on statistical patterns rather than true comprehension. A recent Cornell University study proposed a solution: using multiple AI models to cross-verify outputs. By having a primary model generate results and secondary models vote on their accuracy, the method reportedly reduces errors and achieves 95.6% reliability.

Mira Network’s platform builds on this concept, offering a decentralized system to validate interactions between AI models. Acting as middleware, it adds a layer of checks between users and AI tools, ensuring privacy, accuracy, and scalability. Key applications already leveraging Mira include:

Gigabrain: A DeFi trading platform that filters unreliable AI market predictions.

Learnrite: An education tool that verifies AI-generated exam questions to maintain academic rigor.

Kernel: A blockchain project using Mira to secure AI computations within the BNB ecosystem.

While alternatives like enhanced training or privacy-focused cryptography exist, Mira’s network stands out for its immediate impact and ease of integration. This innovation could expand AI’s role in sectors where precision is non-negotiable.