Global Guidance for Local Generalization in Model Checking
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Abstract (arXiv)
SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for the theory of Linear Integer Arithmetic and implement them on top of SPACER solver in Z3. Our empirical results show that GSPACER, SPACER extended with global guidance, is significantly more effective than both SPACER and sole global reasoning, and, furthermore, is insensitive to interpolation.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2020.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoffcited by 50
- 2025Quokka: Accelerating Program Verification with LLMs via Invariant Synthesiscited by 4
- 2023Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learningcited by 0
- 2026A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checkingcited by 0
- 2026Privilege and confidentiality in generative AI workflowscited by 0
- 2026Privilege and confidentiality in generative AI workflows6 Jul 2026
- 2026A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking23 Apr 2026
- 2025Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis25 Sep 2025
- 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoff26 May 2024
- 2023Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning2 May 2023