Multi-stage LLM workflows can degrade correct outputs when intermediate analyses revise shared state without constraint. We present validation-gated systems for two FinEval 2026 tasks: Japanese Financial Implicit Communication Recognition (JF-ICR) and multilingual financial Q&A (PolyFiQA). Specialized roles produce structured proposals, while task-specific validators admit only scoped label transitions for JF-ICR or source-linked evidence for PolyFiQA. On the 253-example JF-ICR public development set, the final workflow reaches 62.1% accuracy, compared with 51.0% for the stronger direct LLM baseline and 54.9% for a majority-class baseline. Applying all final-stage proposals would yield a net loss of two correct predictions, whereas gated admission yields a net gain of 11. On 76 PolyFiQA training examples, the final workflow yields a modest overall ROUGE-1 improvement (0.3399 to 0.3425) but raises evidence-section ROUGE-1 F1 from 0.1570 to 0.5132. Together, these results suggest that validation-gated updating is most effective for narrow, checkable changes; open-ended synthesis still requires recall-aware admission and closer alignment between validation and answer generation.