This paper describes the CUHKSZ system for the IWSLT 2026 Low-Resource Speech-to-Text task. We propose Gradient-Driven Parameter Sharing (GDPS), a framework that analyzes inter-language gradient behaviors to automatically determine optimal language groupings and shared-private parameter ratios. Built upon SeamlessM4T-Medium, GDPS reduces negative transfer by specializing Layer 11 FFN2 while maintaining shared encoder representations across languages. Additionally, we incorporate curriculum distillation with progressive pseudo-label mixing and test-time reranking combining prior-BLEU weighting and self-consistency scoring. Evaluation on eight low-resource languages (bem, ckb, gle, hau, ibo, yor, aeb, est) demonstrates strongest gains on bem (+2.07 BLEU), hau (+1.50), and ibo (+0.38) compared to unified fine-tuning, while ckb and yor benefit more from prior-based reranking at inference.