Score multiple-choice continuations by their summed conditional log likelihood. Logits and target IDs are already aligned. For batch item $b$, choice $c$ and token position $t$, let $z_{bct}$ be the vocabulary-logit vector, $x_{bct}$ the target ID and $m_{bct}$ the Boolean selection mask. The score is:

The selected choice is:

Here, softmax is over vocabulary entries and log is the natural logarithm. Masked positions contribute zero; do not divide by continuation length. A choice with no selected tokens has score zero, and exact ties select the lowest choice index.

Return a Python dictionary with choice_scores, a PyTorch tensor of shape (batch, choices) matching the logits dtype and device, and predicted_indices, an int64 PyTorch tensor of shape (batch,) on that device.