Can LLM Aid in Solving Constraints with Inductive Definitions?
Solving constraints involving inductive (aka recursive) definitions is challenging. State-of-the-art SMT/CHC solvers and first-order logic provers provide only limited support for solving such constraints, especially when they involve, e.g., abstract data types. In this work, we leverage structured prompts to elicit Large Language Models (LLMs) to generate auxiliary lemmas that are necessary for reasoning about these inductive definitions. We further propose a neuro-symbolic approach, which synergistically integrates LLMs with constraint solvers: the LLM iteratively generates conjectures, while the solver checks their validity and usefulness for proving the goal. We evaluate our approach ...
| Authors | Weizhi Feng, Shidong Shen, Jiaxiang Liu et al. |
|---|---|
| Primary category | cs.LO |
| Published | 4 March 2026 |
| Last updated at source | 12 March 2026 |
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