Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static, manually curated datasets do not satisfy these properties. We present a graph-based evaluation harness that transforms structured clinical guidelines into a queryable knowledge graph and dynamically instantiates evaluation queries via graph traversal. The framework provides three guarantees: (1) complete coverage of guideline relationships; (2) surface-form contamination resistance through combinatorial variation; and (3) validity inherited from expert-authored graph structure. Applied to the WHO IMCI guidelines, the harness generates clinically grounded multiple-choice questions spanning symptom recognition, treatment, severity classification, and follow-up care. Evaluation across five language models reveals systematic capability gaps. Models perform well on symptom recognition but show lower accuracy on treatment protocols and clinical management decisions. The framework supports continuous regeneration of evaluation data as guidelines evolve and generalizes to domains with structured decision logic. This provides a scalable foundation for evaluation infrastructure. Data and Code Availability The WHO IMCI handbook is publicly available (WHO, 2014). Our graph construction, question generation code, and generated question dataset are available at https://github.com/jessicalundin/ graph_testing_harness.

Tokenization inefficiency is associated with structural disadvantages on morphologically complex, low-resource languages, inflating compute resources and reducing accuracy. We evaluate 10 Large Language Models (LLMs) on AfriMMLU (5 subjects; 16 African languages) and show that token fertility reliably predicts accuracy. Higher fertility consistently predicts lower accuracy across all models and subjects. We further find that reasoning models (e.g., DeepSeek, o1) consistently outperform non-reasoning peers across high- and low-resource languages in the AfriMMLU dataset, narrowing accuracy gaps observed in prior generations. In terms of economics, a doubling in tokens results in quadrupled training cost and time, underscoring the “token tax” faced by many languages. These results motivate morphologically aware tokenization, fair pricing, and multilingual benchmarks for equitable natural language processing (NLP).