Multimodal LLM Evaluation Lacks Holistic Assessment.

Despite rapid advancements in multimodal large language models (MLLMs), which can process and generate text from diverse inputs like images, audio, and video, their evaluation methods have not kept pace. Current benchmarks are largely confined to isolated tasks, providing limited insight into an MLLM's ability to genuinely integrate information across different modalities. This fragmented approach fails to reveal whether models possess a holistic understanding of complex, real-world scenarios. The paper identifies several critical gaps in existing MLLM evaluation taxonomies. These include the lack of assessment for temporal-spatial coherence, which involves understanding how elements interact across time and space; physical world understanding, encompassing common sense physics and object interactions; multimodal consistency, ensuring coherent interpretation across different input types; and selective attention, the ability to focus on relevant information from multiple modalities. Addressing these identified gaps is crucial for accurately measuring true progress in multimodal intelligence. Without more comprehensive evaluation frameworks, it becomes difficult to expose the actual capability boundaries of MLLMs and to guide future research and development towards models that exhibit deeper, integrated understanding rather than just performing well on siloed tasks.