A Scalable Four-Level Functional Hierarchy for Evaluating Large Language Models: Hallucination, Self-Monitoring, and the Hypothesized Structural Advantages of Arabic and Chinese

The rapid advancement of Large Language Models (LLMs) has exposed persistent failure modes — hallucination, alignment brittleness, and energy inefficiency — that scale alone has not resolved. We hypothesize that these failures are not incidental bugs but systematic symptoms of a deeper architectural gap: the treatment of language processing as a flat, undifferentiated process rather than a hierarchy of functionally distinct levels. Drawing on established cognitive neuroscience and functional dissociation literature, we propose the Four-Level Functional Hierarchy (FLFH) — comprising Automated Encoding (L1), Symbolic Synthesis (L2), Volitional Self-Monitoring (L3), and Ethical-Teleological Integration (L4) — as a diagnostic and evaluative framework for LLM architectures. We extend a five-criterion Structural Transition Test (STT) to include Recursive Self-Improvement Capacity, addressing digital economy scalability. Under the FLFH interpretation, a systematic analysis suggests that current leading models (GPT-4, Claude, Gemini, DeepSeek) operate predominantly within L1/L2 functional regimes, lacking the L3/L4 integration that the framework posits as necessary for genuine self-monitoring and ethical commitment. We further hypothesize that morphologically rich languages — Arabic and Chinese — may provide structural scaffolding that reduces hallucination rates and improves computational efficiency. Preliminary evidence from AraHalluEval suggests Arabic-specific models achieve statistically fewer hallucinations (p < 0.01), and MorphBPE tokenization improves morphological consistency F1 from 0.00 to 0.66. These findings are interpreted as consistent with, though not conclusive proof of, the FLFH framework. Falsifiability conditions and a validation roadmap are provided to guide experimental follow-up.