What We are Missing in Multimodal LLM Evaluation?
Quick Answer
This paper shows that The evaluation of Multimodal Large Language Models (MLLMs) is lagging behind their rapid advancements, with existing benchmarks failing to assess cross-modal integration.
Quick Take
Key gaps include temporal-spatial coherence and multimodal consistency, which are essential for accurately measuring multimodal intelligence progress.
Key Points
- Current benchmarks focus on isolated tasks, limiting evaluation effectiveness.
- Gaps identified include physical world understanding and selective attention.
- Addressing these gaps is crucial for advancing multimodal intelligence.
- Existing evaluation methods do not measure integration across modalities.
Paper Resources
📖 Reader Mode
~1 min readAbstract:Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced rapidly, evaluation of such models has not kept pace. Most existing evaluation benchmarks are limited to isolated tasks and reveal little about whether a model integrates information across modalities. We examine current means for evaluating MLLMs and review the existing benchmark taxonomy to identify gaps, including temporal-spatial coherence, physical world understanding, multimodal consistency, and selective attention. Addressing these gaps is essential for measuring real progress in multimodal intelligence and exposing capability boundaries.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.26348 [cs.AI] |
| (or arXiv:2606.26348v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26348 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shenghui Chen [view email]
[v1]
Wed, 24 Jun 2026 19:40:53 UTC (5,001 KB)
— Originally published at arxiv.org
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