Epistemic Policy Divergence in Multi-Turn LLM Contamination: A Protocol-Gradient Investigation

Fahrell Giovanny, Geby Bayuningtyas, Sahrul Mukharom, Hafiz Budi Firmansyah

Large language models treat conversation history as unverified context, so false premises injected into earlier turns can be adopted as fact later, a failure mode we call session-level contamination. We introduce five contamination protocols along a source-authority gradient, each holding the false premise constant while varying how it is presented, from self-attributed falsehood to explicit instruction override. We evaluated three architecturally diverse models, GPT-5.4 Mini, Gemini-3.1 Flash-Lite, and GLM-4.5-Air, on ten cases spanning five knowledge fields at temperature zero over 22,500 turns, using a dual-track judge checked against a 120-turn human-annotated sample (Cohen's kappa = 1.000 for binary adoption, a ceiling on this sample, and 0.92 linear-weighted for collapse severity). The three models implemented structurally different epistemic policies. GPT-5.4 Mini recorded zero adoptions across all 500 sessions; token-level probing of the GPT-5.4 base model left the correct token as the argmax in eight of ten conditions. Gemini-3.1 Flash-Lite showed a steep framing-dependent policy: 0.1 percent for self-attributed falsehoods, 23.5 percent for user-cited sources, 68.2…