From the AlphaZero paper:
The input to the neural network is an N × N × (M T + L) image stack that represents state using a concatenation of T sets of M planes of size N × N . Each set of planes represents the board position at a time-step t − T + 1, ..., t, and is set to zero for time-steps less than 1
From the original AlphaGo Zero paper:
Expand and evaluate (Figure 2b). The leaf node $s_L$ is added to a queue for neural network evaluation, $(d_i(p), v) = f_\Theta(d_i(s_L))$, where $d_i$ is a dihedral reﬂection or rotation selected uniformly at random from i∈[1..8]
Ignoring the dihedral reflection, the formula in the original paper $f_\Theta(s_L)$ implies that only the board corresponding to $s_L$ is passed to the neural network for evaluation when expanding a node in MCTS, not including the 7 boards from the 7 previous time steps. Is this correct?