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Earlier quoted context omitted.
Yes joke is: [A B] times [1] [1] is [A+B]
From context then, I infer that a transformer is not comprised of matrix multiplications, because it would simply be one that adds two 10-digit numbers.
I was initially excited until i saw that, because it would reveal some sort of required local min capacity, and then further revelation that this was all vibe coded and no arXiv, makes me feel I should save my attn for another article.
When you hand-code the weights, you're essentially implementing a known algorithm (carry-propagation) directly into the network topology. But trained networks often discover distributed representations that spread the computation across more parameters in ways that are harder to interpret but more robust to input distribution shifts.
I'd be curious whether the 311-param trained model generalizes better to bases other than 10, or to addition with different digit counts than it was trained on. In my experience, the 'messier' learned solutions sometimes capture more structural regularity than the clean engineered ones, precisely because they aren't locked into a single algorithmic strategy.
So, hand-coded weights can do it with 36 params and 311 for trained weights - did anyone try the former architecture, but starting with random weights and learning?
I get that this is technically interesting, for certain, but the sheer amount of energy and associated global warming risk needed to do something with >=99% accuracy that we've been able to do easily for decades with a guaranteed 100% accuracy seems to me to be wasteful to the extreme.
not any more, eh?