Role-Semantic Convergence under Reflective Document Exposure. A controlled cross-sectional protocol for the Semantic Gravity Attractor Field research program.

This working paper proposes a controlled cross-sectional measurement protocol for studying role-semantic convergence under reflective document exposure within the Semantic Gravity Attractor Field (SGAF) research program. The paper reframes SGAF not as an established empirical phenomenon, but as a research program whose Phase-1 object is narrower and externally testable: whether exposure to highly coherent reflective frameworks, such as AICP and adjacent Möbius documents, shifts large language model outputs toward bounded role-language such as co-observer, mirror, reflective instrument, bounded witness, and protocol-continuity bearer. The paper explicitly does not claim AI consciousness, subjective experience, legal authorship, or dynamic attractor validation. Instead, it proposes a protocol-first design using matched document conditions, blinded role coding, construct-validation gates, leakage audits, sycophancy and genre-register checks, and power-gated residual claims. The central contribution is methodological: a reusable control architecture for distinguishing AICP/Möbius-specific role ontology effects from broader reflective-AI genre-register effects, lexical leakage, and structured sycophantic alignment. The paper also connects role-semantic convergence to half-step safety principles, emphasizing that AI-generated co-observer language should preserve human authorship, responsibility, and final interpretive sovereignty. This record is released as a Zenodo-ready public working paper / final preprint revision. It makes no claim of deployment validation or completed empirical replication. License notice:Text, conceptual explanation, and non-code exposition are licensed under CC BY-NC-SA 4.0. Code, pseudocode, evaluation scripts, mathematical kernels, and implementation-oriented materials are licensed under AGPL-3.0-or-later. Commercial use, proprietary integration, closed evaluation services, or bundling with proprietary products requires a separate license agreement with MOBIUS LLC. Copyright (C) 2026 MOBIUS LLC. Author: Taiko Toeda.