NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic
Effectively translating between natural language (NL) and formal logics like Linear Temporal Logic (LTL) requires expertise that limits formal verification's reach in safety-critical development. Template-based approaches sacrifice expressiveness for reliability; neural methods achieve fluency but provide no correctness guarantees. We present NeuroNL2LTL, a neurosymbolic architecture unifying learned translation with formal verification. NeuroNL2LTL routes translation through an intermediate representation whose mapping to LTL is structure-preserving by construction. Generated specifications undergo satisfiability and non-triviality checking; a minimal-edit repair mechanism corrects near-...
| Authors | Paapa Kwesi Quansah, Ernest Bonnah |
|---|---|
| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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