CIll: CTI-Guided Invariant Generation via LLMs for Model Checking
This addresses the scalability problem in model checking for hardware verification, particularly for designs where traditional methods like IC3 produce large clause sets, though it appears incremental as it builds on existing CTI-guided approaches.
The paper tackles the challenge of automatically generating inductive invariants for model checking by introducing CIll, a framework that leverages large language models (LLMs) to synthesize invariants guided by counterexamples to induction (CTIs). The result shows that CIll proved full compliance within the RISCV-Formal framework and full accuracy for all non-M instructions in NERV and PicoRV32 designs.
Inductive invariants are crucial in model checking, yet generating effective inductive invariants automatically and efficiently remains challenging. A common approach is to iteratively analyze counterexamples to induction (CTIs) and derive invariants that rule them out, as in IC3. However, IC3's clause-based learning is limited to a CNF representation. For some designs, the resulting invariants may require a large number of clauses, which hurts scalability. We present CIll, a CTI-guided framework that leverages LLMs to synthesize invariants for model checking. CIll alternates between (bounded) correctness checking and inductiveness checking for the generated invariants. In correctness checking, CIll uses BMC to validate whether the generated invariants hold on reachable states within a given bound. In inductiveness checking, CIll checks whether the generated invariants, together with the target property, become inductive under the accumulated strengthening. When inductiveness fails, CIll extracts CTIs and provides them to the LLM. The LLM inspects the design and the CTI to propose new invariants that invalidate the CTIs. The proposed invariants are then re-validated through correctness and inductiveness checks, and the loop continues until the original property strengthened by the generated invariants becomes inductive. CIll also employs IC3 to work with the LLM for automatically discovering invariants, and uses K-Induction as a complementary engine. To improve performance, CIll applies local proof and reuses invariants learned by IC3, reducing redundant search and accelerating convergence. In our evaluation, CIll proved full compliance within RISCV-Formal framework and full accuracy of all non-M instructions in NERV and PicoRV32, whereas M extensions are proved against the RVFI ALTOPS substitute semantics provided by RISCV-Formal.