What We are Missing in Multimodal LLM Evaluation?

2026-06-24

AI Summary

Current multimodal large language model evaluation benchmarks exhibit significant blind spots in assessing cross-modal integration capabilities, particularly lacking coverage of critical cognitive dimensions such as spatiotemporal consistency, physical world understanding, multimodal coherence, and selective attention. This work addresses this gap by systematically reviewing existing evaluation methodologies and constructing a multimodal cognitive capability taxonomy, thereby explicitly identifying the core cognitive capacities missing from prevailing benchmarks. The study not only reveals substantial deficiencies in current evaluation frameworks but also provides a theoretical foundation and clear direction for developing more comprehensive and cognitively plausible multimodal assessment standards.

Abstract

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.

Problem

• multimodal evaluation
• temporal-spatial coherence
• physical world understanding
• multimodal consistency
• selective attention

Innovation

• multimodal evaluation
• temporal-spatial coherence
• physical world understanding
• multimodal consistency
• selective attention