Alex Gibson

Overview: We train a tiny ReLU network to output sparse top- distributions over a vocabulary much larger than its residual dimension. The trained network seems to converge to a mechanism closely resembling a Bloom filter: tokens are assigned sparse binary hashes, the hidden layer computes an approximate union indicator, and...

Overview: This post builds on Circuits in Superposition 2, using the same terminology. I focus on the z=1 case, meaning that exactly one circuit is active on each forward pass. This restriction simplifies the setting substantially and allows a construction with zero error, with T=D2d2 , L+2 layers, and width...