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This paper proposes a small candidate formalizer trained on offline teacher-generated lists to bridge the interface gap between open-ended LLM workloads and fast decision models, reporting up to 52.6% task success on CALVIN-D with 2.09s end-to-end latency, compared to 11.8% without the formalizer and slower baselines like Qwen3.5-27B.