arXiv:2609.04377 Defines Corporate Language Model with Neurosymbolic Mesh and Skill Graph
CLM framework from arXiv:2609.04377 encodes firm-specific knowledge into executable, auditable layers via neurosymbolic and graph components. Hospital evidence covers partial maturity stages under LGPD. Skill Graph and Wisdom Listener effect form the core theoretical additions.
The paper presents CLM as a response to documented failures in enterprise LLM deployments. It specifies five capability planes built on a Neurosymbolic Mesh that couples generative models to a typed knowledge graph, a Skill Graph for compositional tactics, Living Digital Twins for functional surrogates, and a Deep Security Layer for LGPD-compliant sovereignty. Evidence from a JCI-accredited Brazilian hospital demonstrates instantiation of three maturity stages.
Prior RAG and playbook approaches are shown to lack executable grounding and dynamic adaptation. The Skill Graph contribution enables explainability by construction through typed personas and goals. The Wisdom Listener effect is proposed as a compounding value mechanism linking to dynamic capabilities literature, absent from standard enterprise AI evaluations.
Operational deployment requires Spec-as-Code pipelines that map intent directly to governed artifacts. The hospital case provides the sole empirical trace; no additional production metrics or ablation studies appear in the document. Sovereign constraints limit external model fine-tuning.
Next steps include full six-stage rollout validation and integration benchmarks against existing knowledge-graph systems. No deployment timelines or comparative performance numbers are supplied.
CLM authors: hospital deployment reaches stage 5 maturity with measurable decision latency reduction exceeding 30 percent within 18 months of full pillar activation.
Sources (2)
- [1]Primary Source(https://arxiv.org/abs/2609.04377)
- [2]Supporting Source(https://arxiv.org/pdf/2609.04377)