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@Lagrange Official
As artificial intelligence (AI) integrates deeper into our daily lives, a critical question emerges: how can we trust the outputs of the AI models we rely on? Whether it’s diagnosing medical conditions, making financial decisions, or detecting deep fakes, the need for verifiable AI is paramount. Traditional verification methods often require exposing sensitive model details, risking proprietary information, or rely on institutional assurances that lack technical rigor. Lagrange’s DeepProve, a zero-knowledge machine learning (zkML) inference framework, addresses this challenge by enabling cryptographic proof of AI outputs while preserving model privacy. This article explores how DeepProve works, its scalability through the Lagrange Prover Network (LPN), and its potential to redefine trust in AI.
The Need for Trustworthy AI
AI’s growing role in critical applications demands trust in its outputs. However, verifying the correctness of AI inferences has been a persistent challenge. Conventional approaches often require access to model weights or internal logic, which can expose intellectual property. Alternatively, trust is often placed in centralized institutions that assert the reliability of their systems without providing verifiable proof. This reliance on policy over proof leaves users vulnerable to errors, biases, or malicious manipulation. A global, auditable, and neutral solution is needed to ensure AI outputs are both correct and secure.
DeepProve: Proving AI Outputs with Zero-Knowledge
DeepProve offers a groundbreaking solution by using zero-knowledge proofs (ZK proofs) to verify that an AI model produced a specific output from a given input, all while keeping the model’s weights private. This ensures verifiability without compromising proprietary data, making it suitable for applications ranging from healthcare to finance. DeepProve achieves remarkable efficiency, with proof generation up to 1000 times faster, proof verification up to 671 times faster, and one-time setup up to 1150 times faster than baseline methods.
The framework leverages two key cryptographic techniques:
Sum-check Protocol: This interactive proof method verifies the correctness of computations by representing them as polynomial evaluations. In DeepProve, it efficiently handles linear operations in machine learning models, ensuring accurate verification without revealing inputs.Lookup Argument: This technique optimizes proof generation by checking whether specific outputs have been precomputed, avoiding redundant calculations. It’s particularly effective for non-linear operations like activation functions (e.g., ReLU, softmax) and normalization steps.
How DeepProve Works
DeepProve’s workflow is streamlined for developers and scalable for real-world use. It consists of three main steps:
Preprocessing: A trained machine learning model is exported as an ONNX file, along with sample inputs. The ONNX graph is parsed for efficient proof generation, the model is quantized, and prover/verifier keys are generated. This one-time setup ensures the system is ready for repeated use.Proving: DeepProve processes the model with a given input, generating an execution trace that records how each node in the neural network operates. It produces cryptographic proofs for each node, which are aggregated into a single, succinct proof that confirms the entire model’s correct execution.Verification: Using the model’s input, output, and a commitment that uniquely represents the model without exposing its details, verifiers confirm the inference’s correctness. This process ensures privacy for both the input data and the model.
For developers, the process is straightforward: export the model to ONNX, complete the one-time setup, generate proofs, and verify inferences anywhere.
Scaling with the Lagrange Prover Network
Proving AI outputs with ZK cryptography is computationally intensive, but DeepProve overcomes this through the Lagrange Prover Network (LPN). LPN is a decentralized network of specialized nodes (provers) that generate ZK proofs on demand, acting as a distributed cloud. Lightweight verifiers check the results, ensuring efficiency and accessibility. By distributing proof generation across multiple provers, LPN eliminates bottlenecks, reduces costs, ensures decentralization, and allows optimization for specific models or hardware.
DeepProve splits AI inferences into multiple parts, proves each part independently using parallel machines in LPN, and aggregates them into a compact proof. Specialized prover groups within LPN are tailored for zkML workloads, optimizing for GPUs or ASICs to enhance performance. This modular design ensures DeepProve remains scalable and adaptable as AI and ZK technologies evolve.
LPN also employs a double-auction mechanism called DARA (Double Auction Resource Allocation). Using a knapsack-based algorithm, DARA matches proof requesters with provers based on willingness to pay and compute costs. A threshold pricing model ensures fair pricing: clients pay just enough to secure their bids, while provers receive market-competitive compensation. This creates an efficient, transparent marketplace for cryptographic proofs, making verifiable AI affordable and widely accessible.
The Role of the LA Token
Clients requesting AI inference proofs pay fees based on the required compute, using tokens like ETH, USDC, or LA. Provers are compensated in LA, with a fixed 4% annual token supply distributed to them based on proof volume. This emission model subsidizes costs, allowing clients to pay less while provers are fully rewarded. Token holders can stake or delegate LA to support provers aligned with the ecosystem’s goals, further incentivizing participation.
A Future of Verifiable AI
DeepProve envisions a world where AI outputs are not just trusted but proven. Hospitals can verify diagnostic results without compromising patient data, banks can ensure fair credit decisions, and users can confirm the authenticity of digital content. By combining zero-knowledge proofs with the decentralized power of LPN, DeepProve makes verifiable AI practical, scalable, and accessible.
Conclusion
Lagrange’s DeepProve represents a paradigm shift in AI trust, moving from centralized assurances to cryptographic guarantees. Its efficient proof generation, privacy-preserving design, and scalable infrastructure via LPN position it as a cornerstone for safe and trustworthy AI. As AI continues to shape our world, DeepProve ensures that its outputs can be verified, fostering confidence in the systems we rely on.

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Nadace @Lagrange Official , tým za LA tokenem, oznámila, že může spustit program zpětného odkupu tokenů. Tento krok má za cíl posílit důvěru, snížit volatilitu a podpořit dlouhodobý růst jejího ekosystému.

Proč zpětný odkup tokenu?

Zpětný odkup znamená, že nadace zakoupí LA tokeny z otevřeného trhu pomocí svých rezerv. To může:

Snížit nabídku – méně tokenů v oběhu může vytvářet tlak na růst ceny.

Ukázat důvěru – signalizuje víru nadace v budoucnost projektu.