YANAMI: A Large-Scale Multimodal Dataset and Benchmark For Multi-turn Stateful Interaction

Unified Multimodal Models (UMMs) integrate visual understanding, generation, and editing within a single unified framework. However, existing unified multimodal datasets and benchmarks remain dominated by single-turn tasks. Even in recent multi-turn datasets, many turns can be solved independently without tracking information across turns. Consequently, they provide limited supervision and evaluation for multimodal *stateful interaction*, which requires models to understand, maintain, update, and faithfully realize evolving dialogue states across turns. To address this gap, we introduce **YANAMI** (**Y**our **A**ssistant **N**eeds to **A**ssimilate **M**ultiturn **I**nteraction), the first large-scale multimodal dialogue dataset and benchmark for stateful interaction, comprising **YANAMI-400K** and **YANAMI-Bench**. YANAMI-400K contains 400K multi-turn multimodal dialogues, with more than 2M images and 2.6M dialogue turns. The dialogues span four representative scenarios—Multi-Image Fusion, Image Editing, World Modeling, and Visual Reasoning—and require models to continuously track, preserve, and revise evolving multimodal dialogue states across turns. YANAMI-Bench comprises 400 multi-turn dialogues for systematically evaluating multimodal stateful interaction. It jointly evaluates **Dialogue State Understanding** and **Visual Realization**, characterizing the gap between models' ability to understand evolving dialogue states and their ability to faithfully realize them in visual outputs. Experiments show that fine-tuning BAGEL on YANAMI-400K improves image editing, generation, and multimodal stateful interaction by 10.3%, 2.0%, and 13.23%, respectively, while preserving its general visual understanding capabilities. Evaluations on YANAMI-Bench further reveal that existing UMMs still struggle to maintain, update, and visually realize evolving multimodal dialogue states. Together, YANAMI provides a unified foundation for training and evaluating UMMs toward robust multimodal stateful interaction.