In a 391-session AI collaboration project, LLMs exhibited 'Index Sickness'—a failure where symbolic complexity leads to self-referential outputs disconnected from reality. The 'Pang Principle' asserts natural language conveys superior semantic quality over symbolic systems, and the 'Baseline-Log Physical Separation' mechanism reduced AI instruction volume by 75% and eliminated recurrence of Index Sickness in subsequent sessions.