MMLongBench: Benchmarking Long-Context Vision-Language Models

Long-context vision-language models promise better reasoning over many images and extended inputs, but benchmarks have lagged behind. MMLongBench focuses on that gap by evaluating long-context VLMs across diverse tasks and visual inputs.

The benchmark highlights that strong short-context performance does not necessarily transfer to long-context settings. This pushes model builders to test reasoning and memory under more realistic, extended workloads.

For teams building multimodal systems, MMLongBench offers a more rigorous target for long-context evaluation and model selection.

Paper: https://arxiv.org/abs/2505.10610