Truth Protocol

Inspiration

The Oracle problem is a fundamental challenge in blockchain-based prediction markets: how to securely bring off-chain, real-world data into a blockchain in a way that is trustable and tamper-proof. While traditional oracles provide this data, they are often centralized or rely on multiple trusted sources to reach consensus, which can introduce vulnerabilities or inefficiencies. LLMs offer opportunity to design decentralized, robust mechanisms that can verify information through natural language understanding and context, reducing bias and increasing reliability. The Truth Protocol leverages LLMs to establish an innovative, trustworthy, and automated mechanism to enable transparent and secure prediction markets.

What it does

Truth Protocol uses LLMs to solve the Oracle problem by collecting, interpreting, and verifying real-world data in a decentralized manner. It facilitates prediction markets by providing reliable data feeds through a multi-step process: (1) data is fetched from diverse sources; (2) LLMs analyze and validate the data based on contextual understanding and consensus; (3) the validated data is then recorded on-chain, making it available for prediction markets. This process enhances the reliability of prediction outcomes while maintaining transparency, reducing the risk of data tampering or bias introduced by centralized oracles.

For the MVP, we are using the UMA protocol powering Polymarket.

How we built it

We are mining questions / truths from UMA submissions and then validating them through Mistral models, running them as agents.

Challenges we ran into

It was difficult to ensure structured IO and reliable tool-use for the agent. Curating right links was moderately hard. Writing and listening the right events from Uma protocol and structuring them the right way was also challenging.

Accomplishments that we're proud of

We have a working agent that can validate any statement, or validate the truth within an UMA voter discussion. This integrated with UMA or any Oracle will massively accelerate pulling real world data, not just quantitative but qualitative as well, reliably through Oracles, enabling wider use-cases in prediction markets.

Built With

• agents
• api
• brave
• mistral
• python
• uma