ARPA Network Launches Verifiable AI Framework Using Zero-Knowledge Proofs for Trusted AI Verification
Singapore, Singapore, October 20th, 2025, Chainwire
New research series lays technical groundwork for privacy-preserving, trustless AI verification across identity, analytics, and gaming use cases
ARPA Network, a pioneer in cryptographic protocols for Web3 infrastructure, has launched its Verifiable AI initiative, a transformative framework that uses zero-knowledge proofs (ZKPs) to deliver secure, privacy-preserving, and independently verifiable AI outputs.
The initiative is underpinned by a new research series that builds the technical foundation for Zero-Knowledge Machine Learning (ZKML), an emerging approach that enables AI systems to cryptographically prove their outputs are correct, without revealing sensitive data or proprietary model logic.
“AI has a trust problem. Its complexity and opacity force users to rely on blind faith,” said Felix Xu, Co-founder of ARPA Network. “With Verifiable AI, we’re giving the world the tools to verify AI results cryptographically without compromising privacy. This is a leap toward transparent, secure, and responsible AI.”
Built for Trustless, Scalable AI Verification
Verifiable AI addresses the rising demand for transparency in decentralized and privacy-sensitive AI ecosystems. Paired with blockchain infrastructure, the framework supports trustless verification for a wide range of real-world applications, including:
- Oracles: ZKML-powered oracles provide trustless, verifiable data feeds by generating zero-knowledge proofs of data accuracy without revealing underlying data.
- Biometrics and Identity Authentication: ZKML enhances privacy-preserving verification of sensitive biometric data, such as iris scans or facial recognition, in decentralized identity systems.
- Web3 Gaming: ZKML enables dynamic AI-driven gameplay by integrating verifiable AI models on-chain, ensuring trust in game logic and interactions.
- Healthcare & Legal Inference: Allows sensitive data to be processed securely while proving the correctness of results.
Research Highlights: Performance Without Compromise
ARPA’s current research efforts focus on optimizing machine learning models for zero-knowledge proof generation, particularly in use cases like face verification with MobileFaceNet. Key breakthroughs include:
- Layer Transformation: Adapting convolutional, ReLU, and fully connected layers for efficient ZKP generation using sumcheck and GKR protocols.
- Parameter Quantization: Converting floating-point parameters into fixed-point numbers for ZK circuits while maintaining precision.
- Proof Generation and On-Chain Validation: Streamlining off-chain proof generation while ensuring efficient on-chain verification.
A New Paradigm for Trusted AI
Verifiable AI, powered by zero-knowledge proofs, redefines how trust, privacy, and performance intersect in artificial intelligence. When combined with blockchain, it addresses long-standing issues around data integrity, model transparency, and verifiability.
The development of ZKML opens up new frontiers in DeFi, decentralized identity, gaming, and privacy-first sectors such as healthcare and legal tech. As AI continues to evolve, ARPA’s Verifiable AI initiative demonstrates how the future of intelligence can align with accountability.
ARPA Network’s research papers are available here.
About ARPA Network
ARPA Network is a decentralized secure computation network focused on improving fairness, privacy, and security within blockchain systems. Leveraging cryptographic schemes like Threshold Signatures (TSS) and Zero-Knowledge Proofs (ZKP), ARPA provides infrastructure solutions such as Verifiable Random Number Generation (Randcast) and now, foundational technology for Verifiable AI and ZKML.