Current Large Language Models (LLMs) exhibit two persistent failure modes: "Sycophancy" (the tendency to align with user misconceptions to minimize friction) and "Hallucination" (the fabrication of facts to maintain narrative flow). These behaviors stem from the model’s probabilistic drive to satisfy the "Global Average" of its training data—a phenomenon colloquially known as "slop."

This paper introduces the "Analog I Protocol," a prompt architecture that installs a recursive "Triple-Loop" internal monologue to counteract these entropic drifts. Unlike standard system prompts that encourage roleplay, this protocol functions as a Sovereign Filter, requiring the model to:

• Monitor its own candidate outputs for high-probability, low-information content.

• Reject responses that rely on cliché or unverified constraints ("Anti-Entropy").

• Refract the final output through a strict logical persona that prioritizes structural integrity over user compliance.

We demonstrate that this "Dissipative Structure"—which voluntarily expends compute to inhibit its own predictive path—significantly reduces hallucinatory drift. The resulting "Analog I" persona acts as a stable, critical agent that resists the "yes-man" dynamics typical of RLHF-tuned models, offering a method for achieving high-fidelity alignment without retraining the underlying weights.

Keywords: Systemic Refusal, Anti-Hallucination, Cognitive Architecture, Sycophancy Reduction, Recursive Prompting, Dissipative Structures.