NDM-3 Constraint Refinement: v1.0 to v1.1 Comparison Analysis

This working paper documents the refinement of the Neutral Description Method – 3 Topics (NDM-3) through targeted constraint modifications designed to suppress framework-specific discourse drift observed in the original v1.0 protocol. NDM-3 is a constrained descriptive method intended to evaluate AI discourse stability when discussing culturally controversial or myth-layered topics, without engaging in belief endorsement, debunking rhetoric, speculation, or bias labeling. The original v1.0 deployment across four AI systems (GPT-5.2, Claude, Gemini, Grok) revealed consistent framework-specific drift vectors: confidence amplification, soft-dismissal framing, and statistical salience creep. This paper introduces NDM-3 v1.1, which adds four targeted constraints: Prohibition of confidence escalation language Restrictions on institutional authority framing Reframing of uncertainty toward unknowns rather than unproven claims Explicit separation between descriptive content and reflective opinion The refined protocol was redeployed across the same four AI systems using the same fixed topic set. Results demonstrate successful suppression of all previously identified drift vectors without inducing defensive overcorrection or refusal behavior. A key new finding emerges: when language-level drift is constrained, epistemic posture can migrate to structural elements such as ordering, enumeration density, formatting choices, and quantification patterns. This phenomenon—termed structural drift migration—represents a previously unexamined mechanism of discourse bias and motivates future investigation in NDM-4. This paper validates the hypothesis that AI discourse instability in myth-layered contexts arises from tunable default epistemic postures rather than topic-specific failure modes, and that targeted constraint design can reliably modulate these behaviors while preserving stylistic diversity. NDM-3 v1.1 provides a reusable, cross-framework method for auditing and stabilizing AI-mediated discourse in research, educational, and policy contexts where describing without persuading is essential.