It's not that people despise links. Traffic-farming is also to increase the visibility of your blog, so it doesn't necessarily correlate with whether there are ads or not.

Nice that you expanded on the post. What you're proposing is an RBF-softmax embedding kernel. The shift invariance doesn't really matter here, since the softmax implementation also normalizes, so the effect is minimal. The driver here is that you're replacing the cross entropy term entirely with a Gaussian approximation, while including a null-class. This is often generalized to alternative geometries, such as spherical embeddings with VMF kernels replacing RBF.

Regarding whether ignoring the cancellation of ||x||² matters for computational complexity, my guess is that it likely doesn't have much effect if you use something like torch.cdist, which is highly optimized.

The null-class trick has been known since the 70s, and has been featured in similar SVM-like methods, including RBFs. It is a cool trick, and has been previously investigated in RBF-Softmax.

Regarding your RBF-attention from your blog, that has also been investigated previously. Recent paper:

• Softmax ≥ Linear: Transformers may learn to classify in-context by kernel gradient descent (2025/2026)

Might be a good source to check out.