From LLMs to CORE: Toward a Reflective Architecture for Artificial Intelligence

Large Language Models (LLMs) mark a turning point in the history of artificial intelligence: they produce language with remarkable fluency, yet remain devoid of any internal reflection on their own processes. Their metareflection is entirely linguistic rather than architectural. No mechanisms allow them to monitor or transform their structure in a stable way; consequently, their evolution still depends on massive expansions of data and computational power. This limitation concerns not only efficiency, but the very way intelligence is conceptualized. In this work, we propose an alternative direction that arises directly from these limits: Critical Language Models (CLM) and, beyond them, CORE (Conceptual Ontology for Reflective Evolution). CLMs shift the function of AI from generating language to critiquing language. CORE represents a more radical step: a system that does not grow through increasing amounts of data, but through criteria of internal semantic coherence. We present a minimal ontology composed of six foundational concepts (Entity, Relation, Form, Process, Constraint, Sense), which serves as an interpretive matrix through which the system can reorganize itself over time. The result is a lightweight, reflective, and energy-efficient model capable of developing a form of dynamic identity through an internal Reflective Observer.