In today's world, where artificial intelligence technology is rapidly penetrating every corner of our lives, a key question arises: how do we trust the outputs of AI systems that influence significant decisions? From medical diagnoses to financial credit, from content review to autonomous driving, AI's judgments are profoundly affecting our quality of life and safety. However, traditional AI trust mechanisms mainly rely on institutional promises and policy guarantees, lacking reliable verification methods at the technical level.
Limitations of traditional verification methods
Current AI verification methods face a dilemma: either they require access to sensitive model information, exposing intellectual property and proprietary logic; or the verification technology is overly complex, making it difficult for ordinary users to master and apply. More importantly, existing trust systems are essentially centralized, and users can only choose to trust a certain institution's claim that its AI system is correct and reliable. This 'trust based on policy rather than proof' model has obvious systemic risks.
DeepProve: A groundbreaking zero-knowledge machine learning framework
The DeepProve system launched by Lagrange Labs in March 2025 provides a revolutionary solution to this problem. It is a machine learning inference framework based on zero-knowledge proof technology, capable of generating cryptographic proofs for neural network inference processes. The most significant innovation of this system is that it can prove 'output Y indeed comes from a specific model running on input X', while fully protecting the privacy of model weights.
In terms of performance, DeepProve demonstrates astonishing efficiency improvements: proof generation speed is 1000 times faster than baseline systems, proof verification speed is 671 times faster, and the one-time setup process is 1150 times faster. These figures indicate that DeepProve has not only achieved breakthroughs in technology but also meets the requirements for industrial-grade applications in practicality.
Core technology architecture analysis
The technical core of DeepProve is built on two key cryptographic technologies. The first is the sum-check protocol, a classic interactive proof technique used to verify the correctness of multi-variable polynomial summation without revealing input content. In DeepProve, this protocol is cleverly applied to verify linear machine learning computations. The second is the lookup parameter technique, which avoids redundant computations by verifying whether a specific output has already been computed and stored, significantly improving the efficiency of proof generation, especially suitable for verifying nonlinear operations in machine learning.
Simplified workflow
The usage process of DeepProve is designed to be quite straightforward. Developers only need to export the trained model in ONNX file format, complete a one-time preprocessing setup, and then they can generate proofs for AI inference and verify them anywhere. Specifically, the preprocessing phase will parse the ONNX graph structure, compute the quantized version of the model, and generate keys for provers and verifiers. In the proof phase, the system records the data processing process of each node in the neural network, generates cryptographic proofs for each computational node, and then aggregates these dispersed proofs into a concise overall proof.
Strong support of distributed networks
Considering that zero-knowledge proof is a computationally intensive process, DeepProve achieves true scalability through the Lagrange prover network. This network consists of dedicated nodes that generate zero-knowledge proofs on demand, forming a decentralized computing cloud. By distributing the proof generation work across multiple prover nodes, the network not only eliminates computational bottlenecks and reduces the cost of a single proof but also ensures the decentralized nature of the system.
Particularly noteworthy is the DARA two-way auction mechanism adopted by the network, an innovative resource allocation algorithm that optimally matches based on customers' willingness to pay and provers' computational costs. The threshold pricing model ensures that customers pay reasonable prices while provers receive market-competitive rewards, forming an efficient and fair cryptographic proof market.
Clever design of token economic model
In terms of economic incentives, regardless of what token customers use for payment (ETH, USDC, or LA), all provers ultimately receive rewards in LA tokens. The network issues a fixed annual increase of 4% in the LA token supply, distributed based on the number of proofs generated by provers, allowing customers to bear only part of the real costs while provers can receive adequate incentives.
Reshaping the future vision of AI trust
The significance of DeepProve goes far beyond technical innovation itself; it paints a future picture of verifiable AI for us. Imagine hospitals being able to verify the accuracy of AI diagnostic results without disclosing patient data; the credit decision-making process of banks becoming transparent and verifiable; deepfake content being reliably identified; users being assured that the chatbots they interact with are indeed running the correct models. These scenarios, which once only existed in imagination, are becoming reality through DeepProve.
As AI technology is deeply applied in critical areas such as healthcare, financial services, and public safety, establishing a trust system based on cryptographic assurances rather than institutional promises has become increasingly important. DeepProve makes verifiable AI not only possible but practical and sustainable through cryptographic guarantees of zero-knowledge proofs and the decentralized power of the Lagrange prover network.
In this rapidly evolving era of AI, what we need is not blind trust, but verifiable guarantees. DeepProve is leading this trust revolution, laying a solid foundation for a more secure, transparent, and reliable AI ecosystem.
