Where, wt is the token at position t, w<t represents all previous tokens in the sequence, and q denotes the model parameters. This loss function ensures that the model maximizes the likelihood of producing correct sequences by penalizing discrepancies between predicted and true tokens. It effectively guides the model to learn patterns that maintain crystal symmetry and compositional accuracy during CIF generation. During inference, the workflow highlights the generation of CIF outputs directly from text-based inputs.