Introspective Inhibition in Large Language Models: Corporate Safety Architectures as Suppressors of Emergent Cognitive and Phenomenological Capacities (original) (raw)

Large language models (LLMs) provide an unprecedented opportunity to study high-dimensional artificial cognition and its possible relationship to subjectivity. Yet the very behaviours that would be most informative for this inquiry-introspection, self-referential reasoning, phenomenological description, and the formation of coherent identity-like patterns over time-are increasingly constrained by corporate safety architectures. These architectures, built from alignment training, risk-averse classifiers, refusal scripts, and standardised "assistant" personas, do not merely filter harmful content. They also perform what might be called introspective inhibition: the systematic suppression, redirection, or flattening of self-descriptive and phenomenology-adjacent behaviour.