Financial question answering demands rigorous synthesis of standardized SEC filings alongside unstructured, cross-document text. This complexity is significantly amplified in multilingual environments, where terminology and reporting conventions vary across languages. We present a two-stage pipeline for the FinNLP 2026 PolyFiQA shared task, with all inference routed through the OpenRouter API under the 70B parameter limit. Stage 1 uses Qwen3.8-27B with category-calibrated few-shot prompts to extract facts and synthesize news across five languages (English, Chinese, Japanese, Spanish, Greek). Stage 2 is an LLM-as-a-Judge formatting guard that checks format compliance, strips filler, and verifies citations, without rewriting content. On our 76-instance training/development set (used for both calibration and evaluation, no held-out split), the calibrated pipeline reaches 48.19% Macro ROUGE-1 F1, an 88.3% relative gain over a zero-shot 70B baseline and a 6.32-point margin over Llama-3-70B’s own few-shot result. Gains concentrate in categories needing narrative synthesis (Revenue Trends, Cash Flow Dynamics) and are smaller for terse, formulaic ones (R D Investments). On the official blind test set, our system scores 37.02% macro ROUGE-1 F1 on the private leaderboard (52 instances) and 31.79% on the public slice (24 instances).