WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation

Wei Chow; Jiachun Pan; Yongyuan Liang; Mingze Zhou; Xue Song; Liyu Jia; Saining Zhang; Siliang Tang; Juncheng Li; Fengda Zhang; Weijia Wu; Hanwang Zhang; Tat-Seng Chua

arXiv · 2511.11434

WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation

Wei Chow·Jiachun Pan·Yongyuan Liang·Mingze Zhou·Xue Song·Liyu Jia·Saining Zhang·Siliang Tang·Juncheng Li·Fengda Zhang·Weijia Wu·Hanwang Zhang·Tat-Seng Chua

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Abstract

Recent advances in unified multimodal models (UMMs) have enabled impressive progress in visual comprehension and generation. However, existing datasets and benchmarks focus primarily on single-turn interactions, failing to capture the multi-turn, context-dependent nature of real-world image creation and editing. To address this gap, we present WEAVE, the first suite for in-context interleaved cross-modality comprehension and generation. Our suite consists of two complementary parts. WEAVE-100k is a large-scale dataset of 100K interleaved samples spanning over 370K dialogue turns and 500K images, covering comprehension, editing, and generation tasks that require reasoning over historical context. WEAVEBench is a human-annotated benchmark with 100 tasks based on 480 images, featuring a hybrid VLM judger evaluation framework based on both the reference image and the combination of the original image with editing instructions that assesses models' abilities in multi-turn generation, visual memory, and world-knowledge reasoning across diverse domains. Experiments demonstrate that training on WEAVE-100k enables vision comprehension, image editing, and comprehension-generation collaboration capabilities. Furthermore, it facilitates UMMs to develop emergent visual-memory capabilities, while extensive evaluations on WEAVEBench expose the persistent limitations and challenges of current approaches in multi-turn, context-aware image generation and editing. We believe WEAVE provides a view and foundation for studying in-context interleaved comprehension and generation for multi-modal community.

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Wei Chow, Jiachun Pan, Yongyuan Liang, Mingze Zhou, Xue Song, Liyu Jia, Saining Zhang, Siliang Tang, Juncheng Li, Fengda Zhang, Weijia Wu, Hanwang Zhang, Tat-Seng Chua. 2025-11-14. WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation. https://arxiv.org/abs/2511.11434

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