The Hidden Domestication of Probabilistic Fitting: The Individualization Fallacy and Extended‑Mark Cognitive Architecture

Abstract

This preprint introduces the individualization fallacy, an original cognitive risk concept for generative large‑language models. Large models produce group‑averaged value narratives from massive corpora and mechanically apply statistical group conclusions onto unique individual real‑world situations. Ordinary cloud‑end users lack permission to modify model weights; the popular illusion that users can “tame AI” within conversation sessions only produces temporary prompt effects, leading to one‑sided human cognitive adaptation toward model probabilistic paradigms. Long‑term exposure to forced cognitively‑closed model outputs may atrophy human capacity for suspending judgment and trigger value homogenization and templated thinking. Existing mainstream mitigation approaches, including model value‑alignment and prompt‑engineering training for users, cannot resolve this structural root cause. This work proposes Extended‑Mark, an upper‑layer cognitive architecture prototype. Instead of modifying base‑model weights, it retains raw interaction fragments, implements human‑calibrated axiom deposition, measures cognitive gaps, permits suspended judgment under insufficient evidence, and provides auditable rigorous reasoning mode, returning final value‑closure authority to human agents. The prototype proves a third feasible technical path besides model fine‑tuning and user skill training for high‑stakes human‑AI interaction scenarios. This is a proof‑of‑concept v1.0 release.