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Background

Enshrining ZK proof generation in the critical path of blockchain execution as real-time proving becomes more computationally feasible will be critical to the verifiability of modern AI models on-chain. AI models exhibit high symmetry and structure that we fully exploit to be able to parallelize both the proof generation and verification for verifiability purposes.

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Current State

Zooming out, the zero-knowledge proof ecosystem has seen significant advancement with projects like zkSync, Polygon zkEVM, and Starknet demonstrating the viability of ZK-based Layer 2 scaling solutions for Ethereum.

These platforms leverage different proving systems—from custom-built STARK-based approaches to more general-purpose SNARK implementations—each making distinct trade-offs between proof generation speed, verification cost, and developer accessibility.

Recent developments have focused on making ZK proofs more practical for general computation, with systems like RISC Zero, Aleo, and Miden implementing ZK-native virtual machines that can verify arbitrary computations.

However, the field remains highly fragmented, with each project typically maintaining its own proving stack, circuit development frameworks, and blockchain integration patterns. This fragmentation extends to the tooling ecosystem, where developers must navigate between different circuit languages (like Circom, Cairo, and Leo), proving systems, and blockchain-specific implementations.

While these platforms have made significant progress in reducing proof generation times and verification costs, they still face substantial challenges in scaling to complex computations and achieving the performance requirements needed for widespread adoption in production environments.

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Key Limitations

Current zero-knowledge proving systems and blockchain integrations face several critical limitations that hinder their practical deployment.

The primary challenge is the substantial computational overhead required for proof generation, which can take minutes or even hours for complex computations, making them impractical for real-time applications or high-frequency transactions. Moreover, existing proving systems often require specialized cryptographic expertise to implement correctly, creating a high barrier to entry for developers and increasing the risk of security vulnerabilities.

On the blockchain side, the integration of ZK proving systems faces scalability constraints due to the high gas costs associated with on-chain proof verification, while the limited computational capabilities of existing EVM implementations restrict the types of statements that can be efficiently verified within smart contracts.

Current ZK proving systems are also not optimized for AI workloads, lacking native support for common AI operations like matrix multiplication or activation functions, which makes implementing verifiable AI inference particularly challenging and inefficient. The EVM’s limited instruction set further compounds these issues, as it lacks native operations for handling complex mathematical computations required for both ZK proofs and AI operations, forcing developers to implement these as expensive smart contract operations.

Additionally, most current implementations require trusted setups or rely on complex parameter generation ceremonies, introducing potential security risks and trust assumptions that conflict with blockchain’s trustless nature.

The lack of standardized interfaces and interoperability between different proving systems and blockchains further fragments the ecosystem, making it difficult to build comprehensive privacy-preserving applications that can operate across multiple chains or proving systems.

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Ritual’s Innovation

The EVM++ ZK Proving & Verification Sidecar extends the EVM with native support for zero-knowledge proof generation and verification, enabling developers to seamlessly integrate ZK proofs into their smart contracts without managing complex cryptographic operations directly.

This sidecar abstracts away the intricacies of proof systems, providing a standardized interface for generating and verifying proofs while leveraging optimized proving infrastructure. By incorporating proving capabilities directly into the blockchain’s execution environment, developers can implement privacy-preserving computations, verifiable off-chain execution, and scalable Layer 2 solutions without deep expertise in ZK cryptography.

The sidecar supports multiple proving systems and circuits, allowing developers to choose the most appropriate trade-off between proof size, generation time, and verification cost for their specific use case, while maintaining the security guarantees of the underlying cryptographic protocols.