Hallucination as a Structural Failure: Why Scaled Intelligence Requires Internal Constraint

This paper reframes hallucination in large-scale AI systems not as a technical failure, but as a structural outcome. Drawing on McGilchrist’s hemisphere model, it distinguishes between left-mode abstraction and right-mode contextual grounding—arguing that hallucination arises when abstraction scales without internal mechanisms of restraint. Using LLMs and human institutions as parallel case studies, the paper reveals a consistent failure mode: confidence increases as contact with reality fades, not due to ignorance, but due to a lack of internal answerability. It proposes that scalable intelligence requires architectures where no single mode of cognition can finalize reality without resistance. The paper offers a load-bearing reframing of hallucination—one grounded in structural design rather than epistemic blame—and lays the foundation for recursive regulation in both human and artificial intelligence. IPFS mirror: https://gold-secondary-impala-253.mypinata.cloud/ipfs/bafkreiarzw4rbwotvj4zkuazy4douq633yngnitbtkuziwed55nhhorly4