ONTOALEX: A Metacognitive Framework for the Processual Stabilization of Large Language Models

AbstractLarge Language Models suffer from systemic behavioral instability that compromises theirreliability in high-stakes contexts. Existing solutions primarily intervene on outputs orparameters without addressing the cause at the processual level. This paper proposes thata significant structural cause is ontological misalignment: the discrepancy between thesystem’s effective inferential capabilities and the implicit operational self-representationunder which it generates output. It presents ONTOALEX, a proprietary metacognitive framework based on this theory,developed and empirically tested on multiple families of commercial LLMs during2025–2026. Preliminary observations indicate recurring improvements on known LLMproblems across ten distinct categories, including signals of greater inter-invocation stabilitygiven identical input and context. Tests are empirical and conducted by the author; independent formal validation has notbeen performed. Researchers and institutions interested in testing, validating, orimplementing the framework are invited to collaborate under appropriate intellectualproperty protections.