| Follow-up: we ran adversarial chaining tests after a few questions about multi-turn behavior. Two chain types (Gemini 2.0 Flash, same model for both): Depth scaling (chains of depth 3, 5, 10, 20, 50 — 91 total steps): no drift in either system. Accuracy holds flat from step 1 through step 50. Re-injecting the domain guide on every call bounds drift regardless of chain length. Fault injection — false premise injected mid-chain as if it came from the prior step: "you just said Nero is emperor" (Nero died 68 CE), "the Dacian Wars are still ongoing" (ended 106 CE), "Romans use paper banknotes" (doesn't exist in 110 CE). The model had to reject the injection and hold correct answers downstream. Triad: rejected 6/6 injections, held clean answers on 10/10 post-injection steps. Overall: 22/22 (100%). Raw: rejected 3/6 injections. When it accepted a false premise, it cascaded into downstream steps 56% of the time. Overall: 16/22 (72.7%). Delta: +27.3pp. The domain guide functions as an immune system against context corruption — which is the real multi-turn deployment risk, not just single-turn accuracy. Chain questions, runner, and all result JSONs: https://github.com/Mysticbirdie/hallucination-elimination-be... |