We present a compact, cost-efficient system for the BLP-2025 Bangla Multi-task Hate Speech Identification Task 1, which requires fine-grained predictions across three dimensions: type, target, and severity. Our method pairs strong multilingual transformer encoders with two lightweight strategies: task-appropriate ensembling to stabilize decisions across seeds and backbones; and a multi-task head that shares representations while tailoring outputs to each subtask. As Catalyst, we ranked 7^th on Subtask 1A with micro-F1 73.05, 8^th on Subtask 1B with 72.79, and 10^th on Subtask 1C with 72.40. Despite minimal engineering, careful model selection and straightforward combination rules yield competitive performance and more reliable behavior on minority labels. Ablations show consistent robustness gains from ensembling, while the multi-task head reduces cross-dimension inconsistencies. Error analysis highlights persistent challenges with code-mixed slang, implicit hate, and target ambiguity, motivating domain-adaptive pretraining and improved normalization.