[_hark]

Great point. This points to the related issue: what do we want to compress? Do we want to compress "the answer", here the arithmetic expression's solution, or do we want to compress the image?

You can formalize this with rate--distortion theory, by defining a distortion function that says what your objective is. That implies a well-defined complexity relative to that distortion function.

Okay to be clear, I've written a paper on exactly this topic which will be announced in a week or so. So you won't find anything on the subject yet, haha. But I use almost exactly this example.

[Jerrrry]

  >Okay to be clear, I've written a paper on exactly this topic which will be announced in a week or so. So you won't find anything on the subject yet, haha. But I use almost exactly this example.

I would use Floating Point arithmetic as the example/analogy: one trades off accuracy/precision for exactness/correct-ness when in the extremities. Answers near the more granular representations will be only be able to represented by their nearest value. If this is forsee-able, the floating point implementation can be adjusted to change where the floating point's "extremities'" are.

I used the above analogy and the following when articulating the magnitude of near-lossless-ness that large LLM's have managed to attain, especially when all of the humanities corpus is compressed into a USB flash drive; the Kolmogorov complexity re-mitted/captured is similar to a master-piece like the Mona Lisa having to be described in X many brush-strokes to achieve Y amount of fidelity.