ARC: mechanistic estimation beats sampling for wide random MLPs
ARC researchers estimate random MLP outputs without running the model—using cumulant propagation instead of Monte Carlo sampling.
For wide networks, the mechanistic approach yields tighter bounds in theory and outperforms brute-force sampling in practice.
Work with Wilson Wu, George Robinson, Mike Winer, Victor Lecomte, and Paul Christiano; code and paper both released.