# In the attention mechanism, why don't we normalize after multiplying values?

As this question says:

In scaled dot product attention, we scale our outputs by dividing the dot product by the square root of the dimensionality of the matrix:

The reason why is stated that this constrains the distribution of the weights of the output to have a standard deviation of 1.

My question is why don't we do the same after multiplying to $$V$$(values) for the same reason?

Because what attention does is to control how much of the information in $$V$$ to use based on weights computed through the similarity between $$Q$$ and $$K$$.
When we multiply the attention weights by $$V$$, we are doing a weighed sum of the vectors in $$V$$ to get a new matrix that better represents $$Q$$ contextually within $$V$$. There is no need for variance normalization since its the resulting representation of the attention layer itself, rather than something that we use as a probability distribution. This is in opposition to the product $$QK^T$$, to which we then apply a softmax to obtain dimension-wise pseudo-probabilities.