AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation
Recent image generation models can take multiple reference images as input and combine them into a new image. However, multi-reference image generation remains challenging: models may omit or duplicate subjects from the references, or produce images in which multiple subjects appear unnaturally pasted. Recent work has proposed image generation agents that combine image generation models, reasoning models, and a harness, which is an executable program that specifies how reference images are interpreted, how generation is performed, how outputs are diagnosed, and how the final image is selected. In multi-reference generation, however, references play different roles and outputs must satisfy...
| Authors | Yuta Oshima, Ku Onoda, Yusuke Iwasawa et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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