Research Paper

Verifiable Policy Pipelines for High-Frequency AI Systems

Founder · AnimusLab Research

This research paper evaluates the performance characteristics of cryptographically verifiable governance pipelines, demonstrating sub-millisecond execution times suitable for high-frequency trading and high-assurance AI agents.

Verifiable Policy Pipelines for High-Frequency AI Systems

This paper (currently under preparation for submission to ACM ICAIF 2026) evaluates the computational and cryptographic performance of state-synchronization pipelines for AI guardrails.

Highlights

Verification Latency: Evaluates Canon's sub-millisecond hashing and diffing.
FSB Regulatory Alignment: Connects empirical performance with systemic risk recommendations.
Implementation Evaluation: Provides detailed measurements of SHA-256 state updates and ledger signing operations.

Citation

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Verifiable Policy Pipelines for High-Frequency AI Systems.
AnimusLab Research.
DOI: