Vision Language Models

For years, the dominant approach to multimodal AI followed a simple recipe: take a pre-trained vision encoder (CLIP, SigLIP), bolt it onto a pre-trained LLM through an adapter layer, and fine-tune the connection. This “late-fusion” paradigm powered everything from GPT-4V to LLaVA, delivering impressive results with remarkable sample efficiency. But a fundamental question lingered: was this architectural shortcut an inherent advantage, or merely a convenient workaround? The answer arrived in 2025 with a paradigm shift that’s rewriting the rules of multimodal AI. Native multimodal models—trained from scratch on all modalities simultaneously—are proving that early-fusion architectures don’t just match late-fusion approaches; they exceed them in efficiency, scalability, and ultimately, capability. ...

When GPT-4V describes a meme’s irony or Claude identifies a bug in a screenshot, something remarkable happens: an architecture designed purely for text somehow “sees” and “understands” images. The magic isn’t in teaching language models to process pixels directly—it’s in a clever architectural bridge that transforms visual data into something language models already understand: tokens. Vision Language Models (VLMs) represent one of the most impactful innovations in modern AI, yet their architecture remains surprisingly underexplored compared to their text-only cousins. Let’s dissect how these systems actually work, from the moment an image enters the model to the final text output. ...