Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The communities of blockchains and distributed ledgers have been stirred up by the introduction of zero-knowledge proofs (ZKPs). Originally designed as a solution to privacy issues, ZKPs have now evolved into an effective remedy for scalability concerns. To enable ZKPs, Rank-1 Constraint Systems (R1CSs) offer a verifier for bilinear equations. In order to accurately and efficiently represent R1CSs, several language tools, such as Circom, Noir, and Snarky, have been proposed to automate the compilation of advanced programs into R1CSs. However, due to the flexible nature of R1CS representation, there can be significant differences in the compiled R1CS forms generated from circuit language programs with the same underlying semantics. To address this issue, this article puts forth a dataflow-based R1CS paradigm algorithm, which produces a standardized format for different R1CS instances with identical semantics. Additionally, we present an R1CS benchmark, and our experimental evaluation demonstrates the efficacy of our methods.

The introduction of zero-knowledge proofs (ZKPs) has had a profound impact on the blockchain and distributed ledger communities. ZKPs require the utilization of Rank-1 Constraint Systems (R1CS), which serve as verifiers for bi-linear equations. However, the flexibility of R1CS representation leads to notable variations in the compiled R1CS forms derived from circuit language programs with identical semantics. To tackle this challenge, this paper proposes a data-flow-based R1CS paradigm algorithm, producing a standardized format for different R1CS instances with the identical semantics. By adopting the normalized R1CS format circuits, the complexity of circuits’ verification can be reduced. Furthermore, this paper presents an R1CS normalization algorithm benchmark, and our experimental evaluation demonstrates the effectiveness and accuracy of our methods.