As the training and inference of AI models gradually move towards decentralization, a new problem has emerged: how to prove that the results provided by different nodes are real and trustworthy?
DeepProve offers a solution.
Through zero-knowledge proofs, the results of any node running an AI model can generate a mathematical proof that can be independently verified by all nodes in the network.
This way, the collaborative process of AI no longer relies on the reputation of a single node but on the cryptographic verification of the entire decentralized network.
This allows decentralized AI to move from "usable" to "trustworthy".
Whether it is community collaboration in developing large models or running AI services on a distributed network, DeepProve can help establish new standards of trust.
LA serves both as a governance token and an incentive tool, ensuring that every participant has both the motivation to contribute and the mechanism to supervise others.
