Ranking Function Generation with LLMs for Termination

The paper introduces RAINFALL, a neuro‑symbolic framework that generates ranking functions for proving loop termination by treating synthesis as a code‑completion task with placeholders and verifying the output with a symbolic checker. RAINFALL achieves a 90.22% success rate on 358 terminating C programs, outperforming existing baselines and boosting overall performance to 96.65% when combined with PROTON 2.1. The study also demonstrates the importance of placeholder contracts, few‑shot examples, symbolic verification, and diagnostic feedback across multiple LLMs.

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• llms
• benchmarks