Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications

2026-04-20

AI Summary

This work addresses the challenge of automatically generating semantic-aligned and verifiable formal properties from unstructured natural language requirements. The authors propose a novel large language model (LLM)-based agent architecture that, for the first time, explicitly integrates modeling and verification constraints into the requirement formalization pipeline. Through a modular design, the approach unifies requirement extraction, formalization compatibility filtering, and property translation into a cohesive workflow. Evaluated across three real-world scenarios, the method achieves an accuracy of 77.8%, substantially improving the syntactic correctness, semantic alignment, and verifiability of the generated formal properties.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Early-stage specifications of safety-critical systems are typically expressed in natural language, making it difficult to derive formal properties suitable for verification and needed to guarantee safety. While recent Large Language Model (LLM)-based approaches can generate formal artifacts from text, they mainly focus on syntactic correctness and do not ensure semantic alignment between informal requirements and formally verifiable properties. We propose an agentic methodology that automatically extracts verification-ready properties from unstructured specifications. The modular pipeline combines requirement extraction, compatibility filtering with respect to a target formalism, and translation into formal properties. Experimental results across three scenarios show that the pipeline generates syntactically and semantically aligned formal properties with a 77.8% accuracy. By explicitly accounting for modeling and verification constraints, the approach is a paving step towards exploiting Artificial Intelligence (AI) to bridge the gap between informal descriptions and semantically meaningful formal verification.

Problem

• Requirement Formalization
• Natural Language Specifications
• Formal Verification
• Semantic Alignment
• Safety-Critical Systems

Innovation

• Agentic LLM
• Requirement Formalization
• Semantic Alignment
• Formal Verification
• Natural Language Processing