It is clear to me that adding polynomial features fixes high bias. So far so good. But there is also the claim that adding more (non polynomial) features fixes high bias. I don't see why. In my humble opinion it will not fix it since linear regression will stay linear regression if a new, non-polynomial feature is added. With one feature we will have a straight line in a plain, and by adding one feature we will have a straight plane in a 3 dimensional space.
If you think about it, adding polynomial features is somewhat equivalent to polynomial regression. Instead of adding a polynomial term to the model, you add it to the features. You will end up with the same equations. A polynomial model has less bias because it can fit more complex functions.
Linear regression with two components: $$ LinReg(x_1, x_2) = a_0 + a_1 x_1 + a_2 x_2$$
Polynomial regressions with one component: $$ PolyReg(x_1) = a_0 + a_1 x_1 + a_2 x_1^2$$
If you set $x_2 = x_1^2$ you end up with the same expression.