Deep Field / case

Priya LLM Paragraph

At 7:14 p.m. a paediatric endocrinologist reads an LLM paragraph about her eleven-year-old son's six-week weight loss and thirst pattern—the early picture she recognises as possible Type 1 diabetes while labs wait until Thursday. The fluent paragraph, stabilised in LCD backlight, prices her Stratum-3 fear downward despite a wrong claim mixed among correct ones. The model publishes medical-sounding prose without licence, malpractice carrier, credentialing committee, or trackable prior output—Witness without Canon at Stratum 6. Training prices corpus-fidelity, not truth; Renormaliser burden falls on Priya's clinical renormalisation. What is maintained is only the persuasive token stream. Payment is inference compute plus user-borne renormalisation cost. If she under-trusts without apparatus to audit calibrationally, or the error goes uncaught, the system bears no cost and the next query receives the same architecture—plausible paragraph pricing fear downward with no model-borne accountability.