  With the rapid advancement of multimodal large language models (MLLMs), their
evaluation has become increasingly comprehensive. However, understanding long
multimodal content, as a foundational ability for real-world applications,
remains underexplored. In this work, we present Needle In A Multimodal Haystack
(MM-NIAH), the first benchmark specifically designed to systematically evaluate
the capability of existing MLLMs to comprehend long multimodal documents. Our
benchmark includes three types of evaluation tasks: multimodal retrieval,
counting, and reasoning. In each task, the model is required to answer the
questions according to different key information scattered throughout the given
multimodal document. Evaluating the leading MLLMs on MM-NIAH, we observe that
existing models still have significant room for improvement on these tasks,
especially on vision-centric evaluation. We hope this work can provide a
platform for further research on long multimodal document comprehension and
contribute to the advancement of MLLMs. Code and benchmark are released at
https://github.com/OpenGVLab/MM-NIAH.
