Is AI reasoning afraid of tampering? @Lagrange Official provides cryptographic-level proof with DeepProve

Is the output of AI results tampered with? Can cross-chain data transmission ensure authenticity? These 'trust blind spots' have been the biggest obstacles in the integration of blockchain and AI. The zero-knowledge proof (ZK) solution launched by @Lagrange Official , #lagrange , is innovatively filling the gap, making 'verifiable' a standard feature of AI reasoning and cross-chain computing, equipping blockchain with a 'trust radar'.

This Web3 project deeply engaged in ZK technology has built a complete technical system: a decentralized ZK proof network incorporating over 85 operators, including leading institutions like Coinbase and Kraken, ensuring both security and decentralization for the network; the ZK co-processor innovates the on-chain query experience, making operations as simple as using SQL in daily life, significantly lowering the barrier for developers; the core DeepProve zkML engine enhances the speed of AI proof generation by 158 times, allowing previously hard-to-verify AI reasoning results to quickly generate cryptographic proofs, ensuring that every output is 'authentic'.

$LA token is the 'value anchor' of the ecosystem. Nodes participating in proof tasks must stake LA as collateral; failure to complete on time or errors in proof will result in a deduction of funds, forcing nodes to standardize operations; developers calling the API to use proof services will find LA as the only transaction fee; community participation in network rule revisions allows LA holders to exercise voting rights. Its tokenomics design is rigorous: a total supply of 1 billion, with only 4% released annually, and some tokens periodically burned, tightly binding token value with network activity and actual usage. Now, chains like ZKsync and AltLayer use it to complete cross-chain proofs, and DeFi protocols generate results with proofs back to the chain when querying historical data, completely solving the problem of 'data without certificates'. #lagrange

@Lagrange Official understands developer pain points: no need to study complex ZK mathematical theories, quick access through API calls. Previously, an AI team spent three months building proof circuits to validate model outputs, still encountering numerous vulnerabilities; after integrating with @Lagrange Official , they completed integration the same day, with proof results verified on-chain in seconds. $17.2 million in funding, backed by Founders Fund and EigenLayer, further validates its technical strength and industry prospects. Of course, risks still need to be noted.