According to @DeepLearningAI, the Sample-Efficient Modality Integration (SEMI) framework plugs any pretrained encoder for images, audio, video, sensors, or graphs into an LLM using a single projector plus LoRA adapters generated from a handful of paired examples, enabling multimodal LLMs without massive labeled datasets (source: DeepLearning.AI The Batch). Trained on data-rich domains, SEMI few-shot adapts to new domains and outperforms baselines across tasks, demonstrating strong sample efficiency for multimodal integration (source: DeepLearning.AI The Batch). For crypto and quant teams, the actionable takeaway is reduced labeled-data and ramp-up requirements for deploying multimodal analytics pipelines, though the source does not provide cost metrics or market performance links (source: DeepLearning.AI The Batch).