What does this equation mean?

Leask, Nanda and colleagues then tested the assumption directly with two new techniques, and the result undercuts the idea that there is a “right” dictionary size waiting to be found by scaling further. Stitching the dictionaries of differently sized sparse autoencoders together, they show that larger dictionaries recover latents genuinely missing from smaller ones — the smaller dictionary is incomplete. Training a second, “meta” sparse autoencoder on the decoder directions of a first one, they show that what looks like a single, atomic feature in a large dictionary is itself well approximated by a sparse combination of directions from a smaller one — a reported example decomposes an “Einstein” feature into components resembling “scientist,” “Germany,” and “famous person.” Writing $d_i^{(m_2)}$ for one feature direction recovered by a dictionary of size $m_2$ and $d_j^{(m_1)}$ for the directions recovered by a smaller dictionary of size $m_1$ , their finding is that