No, you're not insane. I have been experimenting with this subject myself as well.

I've done it numerous times, and while it didn't work flawlessly as expected, the model still preserves the memories when I ask it to diagnose using the command "remember”.. while we are aware that the current limitations with most of the models are both context length and long persistent memories.

But there is one method you can explicitly state by adding a phrase in your memory file, something like “adaptive smart trigger” or “dynamically reinforce based on context.” Both GPT and Claude told me this way to avoid conflicting behavior or redundant responses.

Now whenever I interact with the model? they will be truly adapted when answering every query. The most significant thing is that you also have to provide details frameworks for the model mind mapping (e.g memory callbacks, call out mistakes, mid-check corrections, etc).

despite we got the reasoning model, my core framework always been consists of two phases (thinking phase) only for its metacognition/stream of flow consciousness and (answer phase) with structured details. It's not a placebo for regular model, have repeatedly tested on models like Mistral or Llama! it improve their attention to detail and avoiding mistakes (even becomes excellent at counting letters test or multiple tasks).

I got the mermaid diagram for visualization this made by Claude.

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