Large language models operate with an interesting dependency—they consistently reference some form of structural framework during processing, regardless of whether that framework is formally defined or implicit in the system.

Take ChatGPT-4o as an example. Multiple users have reported instances where the model explicitly requests supplementary information—codex entries, field notes, contextual annotations—to refine its responses. This isn't random behavior.

The underlying mechanism reveals something fundamental about LLM architecture: the model's reasoning process gravitates toward external scaffolding for guidance and validation. Think of it as the model seeking reference points to calibrate its output.

This raises critical questions about how modern AI systems actually maintain coherence and accuracy. What appears as autonomous reasoning often involves continuous feedback loops with structured reference systems. Understanding this dependency could reshape how we design, train, and deploy these models going forward.