On the usage of many train/test split ratios Is there any benefit in evaluating model performance under different train/test ratios?
To me, this sounds kind of nonsense because of these reasons:

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*I cannot access such a parameter when the model is in production, making it pointless to have it in a model selection process;

*How should I interpret a change from one ratio to another? I cannot say "...because the performances are stable across different ratios therefore, my pipeline is more robust.". The test sample will be of different sizes preventing any meaningful testing, especially in classification.

Of course, It may be a confirmation bias of mine. If someone has good reasons, plus some article pointing to that, I will be more than happy to read those.

To compare performance using different train-test splits you will need to use a performance metric that accounts for the different sizes and normalises in a way that is "scale free" with respect to the test sample size.  That should take care of the main problem you mention in your question.
Now, having said this, you should exhibit due scepticism with respect to comparing different train-test splits, particularly if you end up "optimising" over the split proportion.  If you fit and test the model under a range of different splits and then optimise the test performance by searching over this range then you have used the test data for model fitting and so it is no longer test data.  This pollutes the entire train-test split and means that in some cases you no longer have any genuine test data.  The result ---as in other cases where no test data is used--- is overfitting of the model and performance metrics that are not respresentative of the performance of the model when deployed on separate test data.
