I say "human-like" in the sense that LLMs are fed in text data largely in the exact form (mapped to tokens) that humans read them.

Thus from first principles it's most likely that content which is more understandable to humans is also likely to be more understandable to LLMs. Of course they are still capable of interpreting very obscure structures too, but usually at the cost of cognitive performance.

I'm open to being wrong about this, and I'm sure it's being researched.

(Specifically for text representations)

To your point, at some level of intelligence an LLM will be able to infer the intent of your prompt consistently without thinking enabled, in which case interpretability to a human matters less. But for complex tasks you aren't likely to get optimal performance with prompts that are difficult for humans to understand. And yes, you'd see that with thinking enabled as it churns over thousands of tokens trying to "mentally expand" a compressed prompt.

Interesting discussion though!