Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation
Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise that most requirement holders lack. Large language models can translate natural-language (NL) requirements into STL, yet stronger translators alone approach an accuracy ceiling. We argue that this ceiling stems from how the task is posed: one-shot, open-loop translation is somewhat ill-defined. Natural language is ambiguous, and, more fundamentally, what a person writes may not always be what they intend, so the target specification is not fully contained in the input text. We therefore reformulate NL-to-STL translation as a closed-loop fee...
| Authors | Bowen Ye, Xiang Yin |
|---|---|
| Primary category | cs.AI |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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