Tabby automates high-performance ZKP circuit generation, achieving efficiency and correctness through synthesis and verification.
Zero-knowledge proof (ZKP) applications require translating high-level programs into arithmetic circuits—a process that demands both correctness and efficiency. While recent DSLs improve usability, they often yield suboptimal circuits, and hand-optimized implementations remain difficult to construct and verify. We present Tabby, a synthesis-aided compiler that automates the generation of high-performance ZK circuits from high-level code. Tabby introduces a domain-specific intermediate representation designed for symbolic reasoning and applies sketch-based program synthesis to derive optimized low-level implementations. By decomposing programs into reusable components and verifying semantic equivalence via SMT-based reasoning, Tabby ensures correctness while achieving substantial performance improvements. We evaluate Tabby on a suite of real-world ZKP applications and demonstrate significant reductions in proof generation time and circuit size against mainstream ZK compilers.
No takes yet. Share an insight, caveat, or question.
Liu et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: