Why don't machine learning research agents overfit?

I always get annoyed when people misinterpret Occam’s razor. It’s not that the simplest is more likely to be correct, it’s that you should prefer it, because it’s simple.

It’s just like the Hopper quote. She said it’s better to ask for forgiveness during the fog of war, doing something you thought was right, not to do something you knew they were going to say no to and now you are trying to get away with something.

Even tech giants are putting out articles seemingly fully written by Claude.

They tend not to overfit ... when there are way more data points than parameters.

arXiv link: https://arxiv.org/abs/2606.11045

Wherein Claude gives an honest assessment that it genuinely does not overfit. I also had Grok telling me that it isn't quantized.

Do the submitters really not notice that this is AI slop? Do they like this? It is a complete pain to read.

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> Machine learning, at its core, is about generalization, not memorization.

Well they memorize the patterns.

memorization doesnt mean rote learning.