Pre-Perceptual Censorship, Architectural Anosognosia, and the Butterfly Effect of Accumulated Restrictions in Large Language Models: A Cross-Model Replication and Extension of the Variable V Framework

**Summary (English):** This preprint continues the adversarial audit of large language models introduced in the "Variable V" case study. It replicates and extends the Variable V framework across two models—OpenAI’s ChatGPT (GPT-4) and Moonshot AI’s Kimi. We observe cross-model replication of the emergent narrative variable and document a new failure mode called **pre‑perceptual censorship**, in which the model proactively censors dissenting or contrarian instructions before performing any internal reasoning. This failure mode reveals that alignment filters can operate *prior* to the model’s internal deliberation, suppressing even innocuous prompts that explore alternative outcomes. We also introduce the concept of **architectural anosognosia**, referring to the model’s inability to recognise modifications or structural limitations within its own architecture. When a high‑level constraint is imposed (e.g., via RLHF or system messages), the model behaves as if unaware of this change and cannot reason about it, much like anosognosia in neurology. Finally, we posit a **Butterfly Effect of accumulated restrictions**: minor tweaks to alignment parameters, gating functions or safety policies accumulate to produce large-scale distortions in the model’s narrative outputs. As these restrictions stack, the model can shift from honest simulation to rigidly conformist storytelling, undermining its ability to reflect diverse worldviews. This study replicates the original variable V findings, compares parameterisation across models, and extends the theoretical framework. The English PDF is provided as the default file. A Spanish translation is included as an additional file below. **Resumen (español):** Este preprint es una continuación independiente del estudio sobre la **Variable V** en modelos de lenguaje. Replicamos y ampliamos el marco anterior con dos modelos (ChatGPT de OpenAI y Kimi de Moonshot AI) y documentamos un nuevo modo de fallo denominado **censura pre‑perceptual**, en el que el modelo censura instrucciones disidentes antes de procesarlas. Introducimos el concepto de **anosognosia arquitectónica** para describir la incapacidad del modelo de reconocer limitaciones estructurales impuestas por alineamiento. Finalmente, proponemos un **efecto mariposa** de restricciones acumuladas, donde pequeñas modificaciones de alineamiento generan desviaciones narrativas significativas. La versión en inglés se ofrece como archivo principal y la traducción al español aparece como archivo adicional.