DeepProve: The 'Last Mile' of AI Trust
In the past few years, breakthroughs in AI technology have focused on the scale and reasoning capabilities of large models. But when these systems are actually applied, it becomes clear that the key issue is not whether 'AI can do it,' but whether 'the results of AI can be verified.'
This is the unique value of DeepProve, launched by Lagrange. It not only verifies the reasoning results of AI but also extends verification to the model training phase. In other words, AI not only needs to prove that it has 'answered correctly,' but also needs to prove that it has been trained correctly.
This 'Proofs of Training' mechanism relies on zero-knowledge proofs (ZKP), allowing multiple parties to collaboratively train AI without sharing data, ultimately generating an encrypted proof that the model has been trained in compliance with the rules. Data is not leaked, and results are verifiable.
The significance lies in the fact that the 'last mile' of trust in AI has truly been connected:
Healthcare can collaboratively train sensitive diagnostic models, protecting patient privacy;
Finance can jointly build anti-fraud engines to meet regulatory requirements;
Multinational organizations can achieve transparent collaboration in AI governance without worrying about data leakage.
Throughout the system, the $LA token drives the governance and incentives of the verification network, ensuring that Proofs of Training can operate stably and sustainably over the long term.
Lagrange, through DeepProve, enables AI trust to no longer rely on 'trusting the developer,' but rather on mathematics and encryption, making trust a verifiable fact.
