QSM-Cutoff: Systematic Derivation of Quantified Cutoff Formulas for Distributed Protocols
Yun-Rong Luo, Aman Goel, Karem A. Sakallah
Abstract
Abstract We introduce , a new procedure that employs the quantified symmetric minimization algorithm from [12] to systematically derive quantified formulas that precisely capture the onset of cutoff and saturation in distributed protocols. performs symmetry-aware forward reachability to enumerate the reachable states of a finite protocol instance, and applies symmetry-preserving logic minimization to express these states as a minimum-cost finitely-quantified reachability formula. repeats this finite analysis process to derive a sequence of reachability formulas R 1 , R 2 , R 3 , ⋯ at increasing protocol sizes. This process terminates at size k when R k is a unique solution to symmetric minimization that yields the exact set of reachable states when evaluated at size k + 1 . We define c : = k as the cutoff size and R c : = R k as the cutoff formula . Empirically, R c is shown to be a reachability invariant that encodes the reachable states for any protocol size. extends the finite analysis process in [12] by introducing two algorithmic enhancements: a depth-first search algorithm that enumerates the reachable states of a finite protocol by searching only for their symmetric quotient, and an extended quantification pattern inference algorithm that expresses explicit clause orbits of finite instances by logically equivalent quantified formulas. Empirical results demonstrate that, compared to the techniques used in [12], is able to analyze a larger corpus of protocols, derive more compact quantified inductive invariants, and converge at smaller cutoffs. In contrast to previous scholarship, offers a new angle for understanding the notions of cutoff and saturation of distributed protocols. In particular, it raises intriguing questions about the unexpected role of symmetric logic minimization in this much-researched area and opens new directions for further research.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get ad5d6792-41b3-4fcd-a35f-188792639942Related papers
- DuoAI: Fast, Automated Inference of Inductive Invariants for Verifying Distributed ProtocolsJianan Yao, Runzhou Tao, Ronghui Gu, Jason NiehOSDI 2022 · 50 citations
- Induction duality: primal-dual search for invariantsOded Padon, James R. Wilcox, Jason R. Koenig, Kenneth L. McMillan et al.POPL 2022 · 14 citations
- Parameterized Verification of Systems with Global Synchronization and GuardsNouraldin Jaber, Swen Jacobs, Christopher Wagner, Milind Kulkarni et al.CAV 2020 · 11 citations
- DistAI: Data-Driven Automated Invariant Learning for Distributed ProtocolsJianan Yao, Runzhou Tao, Ronghui Gu, Jason Nieh et al.OSDI 2021 · 76 citations
- Basilisk: Using Provenance Invariants to Automate Proofs of Undecidable ProtocolsTony Nuda Zhang, Keshav Singh, Tej Chajed, Manos Kapritsos et al.OSDI 2025 · 9 citations
