The evolution of AI models started with unimodal systems, primarily text-only LLMs, which serve as powerful reasoning engines for text. These unimodal models remain highly valuable as specialized experts for specific tasks. Over time, we have entered the era of multimodal AI, where models can process and reason across multiple data types such as text, images, audio, and video.
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Importantly, multimodal AI is often built on top of these powerful unimodal models (The LLM component remains unimodal, serving as the core text reasoning engine), leveraging their reasoning capabilities while extending them to new modalities.
Today, the momentum in AI research and innovation is increasingly centered on multimodal and unified foundation models, capable of integrating any-to-any modalities, yet the strong foundations provided by unimodal models continue to underpin this progress.
