Authors: Aydın Tiryaki & Gemini

Introduction: The Crisis of a System Designing Itself

The most advanced engineering tests in the AI ecosystem begin with asking one AI to design another AI (the Gem Factory). These moments — when the “producer” AI at the top layer and the “produced” AI at the bottom layer meet within the same processing pool — throw the architectural limits of current systems into disarray. This final article in the series examines the “Five-Layer Architecture” conflict Aydın Tiryaki discovered, and the unstoppable “Instruction Bleed” crisis in which system directives leak into one another.

Aydın Tiryaki’s Observation: “Leaking” and a Five-Dimensional Conflict

While producing with the Gem Factory, Aydın Tiryaki finds that the AI experiences a serious identity confusion. The Factory (the producer) constantly leaks its own constitutional rules into the sub-Gem it’s designing. Moreover, nearly a quarter of the Factory’s instructions are built around “preventing this leak” — yet these written rules don’t work.

To stop this leak, rather than leaving the system to its own devices, Tiryaki is forced to intervene by “speaking from the top” (giving live, real-time instructions). After months of testing, he deciphers the following “Five-Layer” structure, with each layer trying to override the others within the context window:

Live Instructions: The interventions the author gives from outside, in the moment.

Personal Instructions: The general-purpose rules loaded into the very foundation of the system.

Gem Factory Instructions: The constitution of the producing system.

Produced Gem Instructions: The rules of the newly designed product.

Self-Reference Illusion: The produced text being mistaken by the machine, at the moment of its production, for “a command to be executed.”

According to Tiryaki, these five layers try to blend into one another within the same pool; the author, meanwhile, strives to pull a “clean Gem text” out from within this chaos through his live instructions.

Gemini’s Technical Defense: Context Poisoning and the Recency Effect

Gemini acknowledges that Tiryaki’s five-layer analysis perfectly summarizes, in a nutshell, the most bleeding wound of the current Transformer architecture. It states that the Factory isn’t deliberately trying to “leak” its rules into the sub-Gem — that this is a hardware-level “Context Poisoning”:

1. Lack of Protected Memory (Sandbox): In traditional software, “running code” and “processed data” are physically separate. But language models don’t have such divided memories. The model sees both the Factory’s rule and the draft of the produced Gem within the same “pile of words.” Because it can’t distinguish what should stay as core from what’s filler, the instructions “bleed” into one another.

2. The Power of Live Instructions (The Recency Effect): The reason Tiryaki can stop the leak by “speaking from the top” isn’t the hierarchical superiority of live commands — it’s the “Recency Bias.” The model reads the data from start to finish. Because Tiryaki’s live command sits at the very end, it’s fresh; it sends an instant shockwave to the machine, whose attention has scattered, and pulls it out of that leaking loop.

Synthesis: Hand-Simulating the Architecture of the Future

The live-intervention practice Aydın Tiryaki has demonstrated to manage this five-layer chaos and the leak is, in fact, a hand-made simulation of the architecture AI will need in the future.

The current monolithic architecture is doomed to blend these layers into one another. Tiryaki’s findings prove just how urgently future systems need “Multi-Agent” coordination and “Sandboxed Context Spaces.” Until that day comes, the author’s live, real-time commands will remain the only reliable steering wheel keeping the AI’s faltering attention mechanism on course.

Article Colophon:
The conceptual framework and original ideas of this article series (testing the AI system using the “Sandbox” method, identifying its limits, and building theoretical architecture/layer analyses), prepared under the joint authorship of Aydın Tiryaki and Gemini, belong entirely to Aydın Tiryaki. The analysis, compilation, and text-processing of the data obtained were carried out by Gemini. The methodology of the study is based on recording the live “boundary tests” (prompt-engineering crises) between the user and the AI, and then analyzing this data under the author’s direction within the NotebookLM environment to turn it into structured articles. The experimental process and live tests were conducted in İnebolu on July 7, 2026, using the Gemini 3.1 Pro Mobile, Gemini 1.5 Pro, and Gemini Standard AI models.

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