We describe our submission to FinEval 2026 Subtask 3 (Multilingual Financial Question Answering). We compare four retrieval strategies and find dense embedding-based retrieval strongest, consistent with its ability to match semantically equivalent content across languages with no lexical overlap. We then ablate prompt-level and inference-time techniques on top of dense retrieval, validating each addition with cross-validation; a category-conditioned router that appeared promising in-sample failed to generalize and was discarded. Our final development-set configuration length-constrained, few-shot, and corrected for a measured English-only citation bias achieves 0.412 ROUGE-1 under 5-fold cross-validation, and our separately tuned test-set submission achieves 0.3275 F1 on the public leaderboard (0.3593 private).