IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
arXiv:2606.09169v1 Announce Type: new Abstract: In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved image-text dialogues is a crucial task for UMMs in real-world applications. However, existing benchmarks fail to evaluate this important task, as they are often limited to single-turn or static settings, and typically overlook exposure bias in multi-turn interactions. To bridge this gap, we propose IMUG-Bench, a comprehensive benchmark for multi-turn interleaved image-text
The proliferation of unified multimodal models necessitates more robust and dynamic evaluation benchmarks to align with real-world complexities, which existing single-turn benchmarks fail to provide.
This new benchmark (IMUG-Bench) will accelerate the development and refinement of more capable UMMs, crucial for building advanced AI systems that can handle complex, multi-turn interactions.
The introduction of IMUG-Bench shifts the standard for evaluating multimodal AI from static, single-turn interactions to dynamic, interleaved understanding and generation, highlighting the shortcomings of current models.
- · AI researchers
- · Generative AI companies
- · Multimodal AI developers
- · AI models reliant on single-turn benchmarks
- · Companies with less sophisticated multimodal evaluation strategies
The benchmark reveals current UMMs' limitations in dynamic, interleaved dialogues, prompting focused R&D into these areas.
Improved UMMs, trained and validated on IMUG-Bench, will enable more versatile and human-like AI agents capable of sustained, complex interactions.
The enhanced capabilities of UMMs could accelerate the deployment of sophisticated AI across various interactive applications, from customer service to creative content generation, potentially impacting white-collar workflows.
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Read at arXiv cs.AI