Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whether this reflects an internal representation of 2D spatial layout or simply the ability to translate spatial descriptions into code. We introduce Autoregressive Mosaics (AM-Bench), a benchmark that separates these factors: First, a translation task gives a model a fully specified geometry of a picture in words as a prompt and asks for the code that produces it. Second, a layout task requires the model to compose an image from an underspecified prompt. Across eight open-weight text-and-code-only models, all models reliably translate specified ...
| Authors | Ashwin Nedungadi, Stefan Oehmcke, Stefan Lüdtke |
|---|---|
| Primary category | cs.AI |
| Published | 31 August 2026 |
| Last updated at source | 1 September 2026 |
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