Unified Multimodal Models (UMMs) integrate visual understanding, generation, and editing within a single unified framework. However, existing unified multimodal datasets and benchmarks remain dominate
YANAMI introduces a large-scale multimodal dialogue dataset and benchmark for stateful interaction, requiring models to track and realize evolving dialogue states across turns, and reveals that current unified multimodal models still struggle with this task.
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, the first large-scale multimodal dialogue dataset and benchmark for stateful interaction,…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
YANAMI: A Large-Scale Multimodal Dataset and Benchmark for Multiturn Stateful Interaction was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #8 of 30, reached on Nov 5, 05:28 UTC. It dropped off the list after Nov 5, 05:28 UTC.
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Not at the moment. It was last on the list on Nov 5, 05:28 UTC, after 2 days of trending.
#8 on Hugging Face · Daily papers, on Nov 5, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 2 days (13 checks) since.