# UniWeaver: Harnessing Unified Multimodal Models for Interleaved Text-Image Generation

**Authors:** Hongxu Chen, Zixuan Zhong, Donghao Zhou, Haodong Li, Xiaomin Yu, Tianhao Li, Shiyao Yu, Zhongwei Wan, Pheng-Ann Heng  
**Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.13530) · [PDF](https://arxiv.org/pdf/2611.13530) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.13530.html)

## Key numbers

- **10,000**: a dataset of 10,000 tasks across 20 task families

## Abstract

Unified multimodal models (UMMs) have opened up new possibilities for generating rich interleaved text-image content. However, they generally lack native support for coordinating the two modalities, and translating coarse user requests into coherent multimodal compositions also requires explicit planning. We propose UniWeaver, a structured, model-agnostic harness framework that integrates multimodal planning, generation, and visual feedback to produce coherent interleaved outputs. To train UMMs for this task, we construct UniWeaver-10K, a dataset of 10,000 tasks across 20 task families, paired with execution trajectories guided by UniWeaver. We also introduce UniWeaver-Bench, a task-oriented benchmark systematically assessing text coverage, writing quality, visual coverage, and image-text alignment. Experiments show that UniWeaver achieves state-of-the-art performance and improves interleaved text-image generation across multiple UMM backbones.

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