Qualia as Compressed Introspective Labels: Interface Opacity and the Appearance of Primitiveness

This paper reframes the problem of qualia as a problem of introspective bandwidth and compressed self-state labels. Rather than claiming to solve the Hard Problem of consciousness, it argues that finite systems cannot transparently access their full internal states, but must rely on low-dimensional, task-relevant summaries for self-monitoring, control, and report. On this view, experiences such as pain, fear, fatigue, or hunger are not non-physical additions to bodily processes, but compressed introspective labels integrating distributed physiological signals, attentional capture, action tendencies, memory marking, and report readiness. Their apparent immediacy and irreducibility may arise from the fact that the system accesses the compressed label while lacking access to the process that constructs it. The paper further connects this framework to artificial intelligence by distinguishing external verbal output, externally decodable internal representations, and compressed self-states that are actually used by the system. The argument does not claim that current AI systems possess phenomenal consciousness; instead, it proposes a more operational target for studying consciousness, introspection, and AI interpretability.