Single-pass decision models via vocabulary masking for efficient inference

Decision models like Jev reduce inference overhead by masking non-target tokens in the vocabulary to force a single-pass selection from fixed options. This approach eliminates the multiple sequential passes required by standard autoregressive generation for structured outputs like JSON.