Earlier Sign Language Production (SLP) models typically relied on autoregressive decoding, which naturally preserves temporal causality but suffers from error accumulation at inference time. More recent diffusion-based approaches improve generation quality through iterative denoising, yet their sequence-level refinement process introduces substantial latency. To address this trade-off, we propose HybridSign, a hybrid autoregressive-diffusion model for low-latency sign language production that combines causal frame generation with flow-based diffusion refinement. A Multi-Scale Pose Representation module captures fine-grained articulator features, while a Confidence-Aware Causal Attention mechanism leverages joint-level confidence scores to improve robustness under noisy 2D pose observations. Experiments on PHOENIX14T and How2Sign show that HybridSign consistently achieves the best quality--efficiency trade-off among the compared baselines. On the How2Sign test split, it reaches BLEU-1/4 scores of 30.12/6.48 and DTW of 3.89, while reducing time-to-first-frame to 5.90s and increasing throughput to 10.17 FPS under a 60-frame evaluation protocol.