AeroReq2LTL automates LTL generation from industrial aerospace requirements via LLMs with a data dictionary and templates, achieving 85% precision and 88% recall on real data.
URL https://arxiv.org/abs/2512
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.SE 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.
A prototype framework collects legal requirements and translates them into machine-actionable policies for federated data processing networks via policy-as-code and LLMs.
citing papers explorer
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Automated LTL Specification Generation from Industrial Aerospace Requirements
AeroReq2LTL automates LTL generation from industrial aerospace requirements via LLMs with a data dictionary and templates, achieving 85% precision and 88% recall on real data.
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Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.
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Compliance Management for Federated Data Processing
A prototype framework collects legal requirements and translates them into machine-actionable policies for federated data processing networks via policy-as-code and LLMs.