Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Although Unified Multimodal Models (UMMs) can jointly generate text and images, multimodal i
IntReal introduces a real-world interleaved multimodal dataset and evaluation framework to diagnose unified model strengths and improve generation through targeted training.
Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Although Unified Multimodal Models (UMMs) can jointly generate text and images, multimodal interleaved generation remains largely underexplored. Progress in this area is hindered by two key limitations: (1) the lack of well-defined interleaved generation tasks and corresponding training data, and (2) the absence of fine-grained evaluation protocols that can reliably diagnose model performance across different capability dimensions. To this end, we present IntReal, comprising two complementary components: IntReal-100K, a real-world dataset of multimodal interleaved samples spanning…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 38 checks on 4 different days. Its best position was #15 of 30, reached on Nov 2, 05:28 UTC. It dropped off the list after Nov 2, 05:28 UTC.
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#15 on Hugging Face · Daily papers, on Nov 2, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 4 days (38 checks) since.