The CLM framework addresses the limitations of generic LLMs and RAG systems by integrating tacit knowledge, ontological grounding, and governance. It combines a Neurosymbolic Mesh with a knowledge graph, supporting reasoning and decision-making.
Engineers can leverage CLM's Skill Graph for compositional explainability, enabling reuse of tactics, personas, and goals. The Living Digital Twins model functional areas as reasoning surrogates, facilitating organizational learning.
The architecture includes a Deep Security Layer to enforce sovereignty, traceability, and human oversight, and a Spec-as-Code paradigm to connect grounded intent with executable artifacts. This approach aims to improve enterprise AI deployment reliability.
Source: https://arxiv.org/abs/2609.04377