We describe the Saudi Legal FC submission to the AISA-ArabicFC shared task on Arabic function calling. Our system is a parameter-efficient fine-tune of Qwen2.5-7B-Instruct with LoRA, trained with completion-only loss masking. Beyond the model, our central contribution is methodological: we use written, falsifiable score predictions and a validated local scorer to gate submissions, with a post-processing safety net that uses no gold at inference. On the public development set our best configuration reaches Argument Exact-Match (ArgEM) 0.828 and function-name accuracy 1.0 (Overall-B 0.914) under the official v1.3 evaluator. Its weights were then lost; the blind submission comes from a reconstruction selected against that model’s frozen predictions, and ranks 12th on Track B with Overall-B 0.8946. We also report two negative findings—one retraining run, one rejected hypothesis—and an artifact-identity failure our behavioral checks caught before submission.