Given a set of points on a sphere, how can I implement a higher order low pass filter on them?

At the moment, I am just multiplying the vectors from the input and output set by their weights and summing them, component-wise. While this works well when the angles are small, the filter response isn't right as the angles get larger than 30 degrees. It needs to perfectly compliment the response of a high pass filtered gyroscope, which works with angles, not vectors. 

What I'm essentially looking for is an interpolation method for multiple (in my case 8) weighted points on a sphere. The sum of all weights is one. The algorithm needs to be such, that if it were applied to only two vectors $a$ and $b$, with the weights $(1 - t)$ and $(t)$ respectively, a constant rate of change in *t* would result in a constant *angular* rate of change in the output. Say, for example we had as our two vectors $a = (1, 0, 0)$ and $b = (0, 1, 0)$, the angle *ab* is 90 degrees. If we set $t$ to $1/3$, we should expect the result, $c$, to be 30 degrees away from $a$. If we use the standard vector multiplication rules to calculate $c$ as $a\*(1 - t) + b\*t$, the angle between $a$ and $c$ is closer to 22 degrees. This error gets worse as the angle $ab$ increases. I know that the slerp algorithm exists for just two points, but I need one that works on any number of points.

The problem is that standard linear interpolation between points on a sphere represented as vectors, does not interpolate correctly when you look at the angles.