What We are Missing in Multimodal LLM Evaluation?

Computer Science > Artificial Intelligence [Submitted on 24 Jun 2026] What We are Missing in Multimodal LLM Evaluation? Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu 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 Focus to learn more Submission history From: Shenghui Chen [view email] [v1] Wed, 24 Jun 2026 19:40:53 UTC (5,001 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)