Vision-Language Models — The ViT-MLP-LLM Pattern
A vision encoder converts an image into tokens. An MLP projector maps those tokens into the LLM's embedding space. A language model does the rest. That pattern — ViT-MLP-LLM — is every production VLM in 2026. State the ViT-MLP-LLM architecture and explain what each of the three components contributes. Compare Qwen3-VL, InternVL3.5, LLaVA-Next, and GLM-4.6V on parameter count, context length, and benchmark performance. Explain DeepStack: why multi-level ViT features tighten vision-language alignment better than a single last-layer feature. Measure VLM hallucination in production with Cross-Modal Error Rate (CMER) and act on the signal. CLIP (Phase 4 Lesson 18) gives you a shared embedding space for images and text, which is enough for zero-shot classification and retrieval. It cannot answer "how many red cars are in this image?" because CLIP does not generate text — it only scores similarities. Vision-Language Models (VLMs) — Qwen3-VL, InternVL3.5, LLaVA-Next, GLM-4.6V — bolt a CLIP-family image encoder to a full language model. The model sees an image plus a question and generates an answer. In 2026 open-source VLMs rival or beat GPT-5 and Gemini-2.5-Pro on multimodal benchmarks (MMMU, MMBench, DocVQA, ChartQA, MathVista, OSWorld). The trio of pieces (ViT, projector, LLM) is the standard. The differences between models are in which ViT, which projector, which LLM, the training data, and the alignment recipe. Once you understand…
Vision-Language Models — The ViT-MLP-LLM Pattern: A vision encoder converts an image into tokens. An MLP projector maps those tokens into the LLM's embedding…
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