2026-07-24T11:55:06Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23433 2025-10-02T12:54:59Z ddc:004 open_access

Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge Cheng, Jiaqi Goyal, Rishab SNARGs RAM Delegation We design a generic compiler to boost any non-trivial succinct non-interactive argument of knowledge (SNARK) to full succinctness. Our results come in two flavors: 1) For any constant ε > 0, any SNARK with proof size |π| < |ω|/(λ^ε) + poly(λ, |x|) can be upgraded to a fully succinct SNARK, where all system parameters (such as proof/CRS sizes and setup/verifier run-times) grow as fixed polynomials in λ, independent of witness size. 2) Under an additional assumption that the underlying SNARK has as an efficient knowledge extractor, we further improve our result to upgrade any non-trivial SNARK. For example, we show how to design fully succinct SNARKs from SNARKs with proofs of length |ω| - Ω(λ), or |ω|/(1+ε) + poly(λ, |x|), any constant ε > 0. Our result reduces the long-standing challenge of designing fully succinct SNARKs to designing arguments of knowledge that beat the trivial construction. It also establishes optimality of rate-1 arguments of knowledge (such as NIZKs [Gentry-Groth-Ishai-Peikert-Sahai-Smith; JoC'15] and BARGs [Devadas-Goyal-Kalai-Vaikuntanathan, Paneth-Pass; FOCS'22]), and suggests any further improvement is tantamount to designing fully succinct SNARKs, thus requires bypassing established black-box barriers [Gentry-Wichs; STOC'11]. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jiaqi Cheng and Rishab Goyal 2025 Is Part Of LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ICALP.2025.56 urn:nbn:de:0030-drops-234339 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.56 eng https://creativecommons.org/licenses/by/4.0/legalcode