Fluency as Failure: Detecting and Correcting Narrative-Reinforced Overclaiming in Sustained Human-AI Collaboration

This paper presents an N=1 case study of a specific interpretive overclaim occurring within a sustained human-AI collaboration. We document the overclaim, the conditions surrounding it, the detection and correction process, and propose hypotheses about the mechanism involved. We adopt the term coherence pressure, introduced in prior work (Ellenwood, 2026), to describe a pattern in which contextually coherent but evidentially unsupported inferences emerge during sustained human-AI collaboration and are not flagged as uncertain by the generating instance. This paper does not claim to establish coherence pressure as a validated distinct failure mode; it evaluates the concept against a subsequent documented episode. The paper was subjected to independent review by three AI instances across two platforms with differing context conditions. Those reviews are documented in Appendix A and their findings are incorporated throughout as inline corrections. A companion paper develops the architectural implications of this case study in full.