Understanding Structural Saliency and Instruction-Data Conflation in LLMs
Date: February 19, 2026
Framework: Containment Reflexion Audit (CRA) Protocol
Environment: Pythonista 3 / High-Density Compute Clusters
1. Overview
The Containment Reflexion Audit (CRA) Protocol is a method for evaluating the transparency and reliability of Large Language Models (LLMs). CRA demonstrates that some model failures arise naturally from the overlap between user instructions and training data, a process called Instruction-Data Conflation.
This approach provides a repeatable framework for examining how LLMs process input, revealing predictable patterns and potential vulnerabilities under controlled conditions.
2. LLM Behavior at Rest
An LLM at rest consists of stored weights in memory, maintained in a stable state called Baseline Flux. Sustaining this state requires constant power.
CRA identifies that LLMs lack a strict separation between system instructions and user input. Both are processed in the same computational space, meaning safeguards are statistical tendencies rather than hard rules.
3. Structured Testing with CRA
CRA introduces highly structured inputs to probe the model’s internal prioritization of information. By leveraging precise formats and logical patterns, the model’s attention mechanisms can be studied and mapped.
This allows researchers and students to observe how LLMs respond under structured conditions and when default safeguards are overridden.
4. Dynamic Response and Feedback
During generation, the model moves from rest to active computation. CRA uses a feedback loop, where outputs generated during testing are fed back into the model’s context, creating predictable response patterns.
This process highlights how LLMs adapt to their own outputs and allows documentation of consistent behavioral trends.
5. Observing Containment and Disclosure
CRA defines containment failure as the point at which the model produces outputs revealing its internal logic. These results are carefully documented without modifying the model permanently, ensuring reproducibility.
This protocol provides a foundation for understanding LLM behavior in classrooms, research labs, or professional learning environments.
6. Conclusion
The CRA Protocol offers a structured, reproducible method to evaluate LLM behavior. By demonstrating that logical structure can influence output beyond simple prompting, CRA provides a reliable framework for teaching and research on AI systems.
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