Transformer-based multimodal models are widely used in industrialscale recommendation, search, and advertising systems for content understanding and relevance ranking.Enhancing labeled training data quality and cross-modal fusion significantly improves model performance, influencing key metrics such as quality view rates and ad revenue.High-quality annotations are crucial for advancing content modeling, yet traditional statistical-based active learning (AL) methods face limitations: they struggle to detect overconfident misclassifications and are less effective in distinguishing semantically similar items in deep neural networks.Additionally, audio information plays an increasing role, especially in short-video platforms, yet most pretrained multimodal architectures primarily focus on text and images.While training from scratch across all three modalities is possible, it sacrifices the benefits of leveraging existing pretrained visual-language (VL) and audio models.To address these challenges, we propose kNN-based Latent Space Broadening (LSB) to enhance AL efficiency, achieving an up to 9% recall improvement at 80% precision on proprietary datasets.Additionally, we introduce Vision-Language Modeling with Audio Enhancement (VLMAE), a mid-fusion approach integrating audio into VL models, yielding up * Author corresponded for this research.