Research line · ascendant
Native-resolution vision-language models
Process images at their own resolution and aspect ratio, with position encodings general enough to handle the variation.
What defines membership
Resizing every image to a fixed square destroys exactly the detail that grounding and text rendering depend on, and the fix belongs in the architecture.
How the line developed
Read top to bottom: what came before, the idea itself, the evidence for it, what improved, and where it breaks.
The idea
Qwen2-VL · 2024-09core
Native dynamic resolution plus a rotary position encoding decomposed across time, height and width.
Improvement
Qwen2.5-VL · 2025-02core
Adds windowed attention and absolute time encoding; becomes the text encoder inside Qwen-Image and the instruction parser inside Step1X-Edit.
InternVL3 · 2025-04core
Pretrains multimodally from the start rather than adapting a finished text model.
At scale
Qwen3-VL · 2025-11emerging
Interleaved multimodal rotary embeddings and multi-level vision feature fusion.
What it gets right
- Preserves the fine detail that text rendering and grounding need
- Directly load-bearing for generation: this family encodes the prompts for much of current open text-to-image and editing work
- Handles wildly varying aspect ratios without a preprocessing compromise
Where it is weak
- Variable sequence length complicates batching and serving
- Position-encoding schemes are proliferating faster than they are being compared against each other
- Most of the evidence is from one lab's model family