We describe our submission to Subtask 3 of the FinNLP 2026 Shared Task, based on MultiFinBen/PolyFiQA (Peng et al., 2026), in which models answer cross-document financial questions using SEC financial-report statements and contemporaneous multilingual news in English, Chinese, Japanese, Spanish, and Greek, evaluated using ROUGE-1. The task’s four fixed question templates require an answer synthesized from the news, supported by evidence cited from the financial statements. We use Qwen3-VL-30B-A3B-Instruct, an open-weight mixture-of-experts model accessed via OpenRouter, with template-aware prompting for each question type. In early development, a multi-stage pipeline separating answer generation from evidence retrieval produced a high rate of empty evidence fields. A single unified generation call substantially reduced this rate. We additionally find that several formatting conventions, including specific numeric notation, compact period references, and a fixed field label, were not reliably followed through prompt instructions alone, and were instead enforced with rule-based post-processing. Our final system achieves a ROUGE-1 F1 of 36.53% on the official test set. These results suggest that deterministic post-processing remains a practical complement to prompt-based instruction for enforcing output conventions that models do not reliably learn from context alone.