2025/11/30 by PIerre Dantas, Pierre Dantas, Lucas C. Cordeiro +7 · 8 voices
Computer Science · #Software Engineering Research #Security and Verification in Computing #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2512.02080
The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows. However, current methods remain unreliable: without a solid theoretical footing, the refinement process acts as a black box that may oscillate, loop, or diverge. This work bridges this critical gap by developing an LLM-Verifier Convergence Theorem, providing the first formal framework with provable guarantees for termination in multi-stage verification pipelines. We model the interaction not as a generic loop, but as a sequential absorbing Markov Chain comprising four essential engineering stages: CodeGen, Compilation, InvariantSynth, and SMTSolving. We prove that for any non-zero stage success probability (δ> 0), the system reaches the Verified state almost surely. Furthermore, because of the sequential nature of the pipeline, we derive a precise latency bound of 𝔼[n] ≤ 4/δ. We stress-tested this prediction in an extensive empirical campaign comprising over 90,000 trials. The results match the theory with striking consistency: every run reached verification, and the empirical convergence factor clustered tightly around Cf≈ 1.0, confirming that the 4/δ bound accurately mirrors system behavior rather than serving as a loose buffer. Based on this data, we identify three distinct operating zones -- marginal, practical, and high-performance -- and propose a dynamic calibration strategy to handle parameter drift in real-world environments. Together, these contributions replace heuristic guesswork with a rigorous architectural foundation, enabling predictable resource planning and performance budgeting for safety-critical software.