A Normative Intermediate Representation for ASP-Based Compliance Reasoning

Researchers propose MONIR, a normative intermediate representation framework for automated compliance reasoning using Answer Set Programming (ASP). The system combines staged operational semantics with executable ASP compilation to evaluate regulatory adherence, demonstrated through application to Chinese ADAS (Advanced Driver Assistance Systems) regulations with LLM-assisted extraction pipelines.

MONIR represents a technical advancement in computational compliance verification, addressing a growing need for automated regulatory reasoning in safety-critical systems. The framework bridges formal logic and practical implementation by providing both theoretical foundations through staged semantics and executable solutions via ASP compilation, enabling organizations to systematically verify adherence to complex regulatory standards.

The application to Chinese ADAS regulations reflects broader global momentum in autonomous vehicle standardization. As nations establish competing frameworks for vehicle safety systems, automated compliance checking becomes essential for manufacturers navigating multi-jurisdictional requirements. The integration of large language models for extraction pipelines suggests practical applicability beyond academic contexts, potentially reducing manual compliance review workloads.

For the automotive and compliance technology sectors, this work demonstrates feasibility of formal verification methods for real-world regulatory documents. Developers of compliance software could leverage ASP-based approaches to build more sophisticated policy engines. The modular and incremental solving capabilities mentioned suggest scalability advantages for complex regulatory environments, particularly relevant as regulations grow increasingly sophisticated.

Future developments worth monitoring include expansion to other regulatory domains beyond ADAS, deeper integration with machine learning for document interpretation, and adoption by compliance platforms serving manufacturers. The research validates that formal methods can handle naturalistic regulatory text when combined with modern NLP techniques, potentially enabling broader computational governance applications.

• →MONIR provides automated compliance reasoning framework combining formal semantics with practical ASP execution for regulatory verification
• →LLM-assisted pipeline enables extraction of compliance requirements from natural language regulatory documents
• →Framework demonstrates efficiency gains through modular and incremental solving approaches
• →Application to Chinese ADAS regulations shows viability for safety-critical automotive standards
• →Scalable approach could extend to other regulatory domains requiring formal compliance verification