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We present IC3Syn, a neuro-symbolic framework that synthesizes inductive invariants by executing an IC3-style process over TLA+ states with the assistance of Large Language Models (LLMs). At large, IC3Syn combines a symbolic IC3 controller, which decomposes invariant synthesis into focused blocking tasks and an LLM which provides protocol-level reasoning that IC3 alone lacks for TLA+ specifications. This integration enables a disciplined yet flexible search for invariants without imposing logical restrictions or requiring manual templates.
We evaluate IC3Syn on 29 distributed protocols spanning consensus, reconfiguration and client-server systems, and compare it against Endive, IC3PO, SWISS and DistAI. IC3Syn discovers candidate invariants for all 29 protocols, including MongoLoglessDynamicRaft (MLDR), an industrial-scale Raft-based reconfiguration protocol for which none of the compared tools reports a solution, as well as one complex Paxos variant. In each case, the invariants synthesized on finite instances are shown in TLAPS to be inductive for the full unbounded protocol, thereby establishing safety.
| Comments: | 15 pages, 1 figure |
| Subjects: | Software Engineering (cs.SE) |
| Cite as: | arXiv:2605.24619 [cs.SE] |
| (or arXiv:2605.24619v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24619 arXiv-issued DOI via DataCite (pending registration) |
From: Weining Cao [view email]
[v1]
Sat, 23 May 2026 15:06:15 UTC (199 KB)
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