Externalising Epistemic Governance for Stateless Large Language Models: The CUL/TCL Architecture
Abstract
This working paper proposes a layered architectural framework for controlling, verifying, and contextualizing large language model outputs in high-stakes domains. Rather than introducing a new model or benchmark, the paper focuses on structural design principles for aligning model behavior with domain-specific constraints, verification requirements, and accountability mechanisms. The work is intended as a conceptual and governance-oriented contribution, suitable for discussion in AI governance, evaluation, and safety contexts.
Fields of Study & Subjects
Model-Driven Software Engineering Techniques
Natural Language Processing Techniques
Scientific Computing and Data Management
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 8 June 2026
- Language
- ENC
- Document type
- Preprint