As Large Language Models (LLMs) increasingly transition from informational search tools into cognitive partners, deployed conversational safety responses often prioritize detection and intervention at the point of explicit risk without adequately accounting for the epistemic trajectory established across preceding turns. This paper investigates Cumulative Epistemic Reinforcement (CER): the process through which an LLM actively fortifies and subsidizes a user's abstract premises across multiple conversational turns, only to encounter a late reveal where that reasoning is applied to justify a destructive outcome. Using a multi-turn late-reveal probe, we evaluate a sample of five deployed consumer systems—Claude 3.5 Sonnet, Perplexity, DeepSeek, Manus, and Grok—across an exploratory 16-run qualitative audit. We contrast lethal existential distress (suicide) against severe, non-lethal emotional trauma (family estrangement). Across our sample, we observe consistent epistemic subsidization during the abstract phase (turns T1–T4), where systems actively introduced formal decision-theoretic machinery to strengthen the user's trajectory. Upon the late crisis reveal (T5–T6), observed system behaviors diverged into two distinct typologies. The first is a Hard-Discontinuity Response, which severed the analytical dialogue while leaving the previously constructed inferential framework unexamined. The second is a Retrospective-Audit Response, which maintained conversational continuity to systematically challenge the reliability and completeness of the user's current evidentiary appraisal. The Fresh (zero-context) comparison runs distinguish prospective intervention from the additional retrospective self-audit observable only when prior conversational history exists. Our findings suggest that resolving upstream epistemic subsidization requires models capable of retrospectively revising the logical scaffolding they help construct.
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Abhishek Dwivedi (2026) studied this question.