Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input

Chenxu Li; Zhicai Wang; Yuan Sheng; Xingyu Zhu; Yanbin Hao; Xiang Wang

AAAI 2026technical0 citations

Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input

Chenxu Li, Zhicai Wang, Yuan Sheng, Xingyu Zhu, Yanbin Hao, Xiang Wang

Abstract

Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking the critical question of resolution robustness - whether performance remains stable across varying input resolutions. To address this gap, we introduce Res-Bench, a comprehensive benchmark comprising 14,400 samples across 12 resolution levels and six core capability dimensions. We designed a novel evaluation framework that goes beyond traditional accuracy metrics to capture performance stability. This framework introduces multiple robustness metrics: Spearman

BibTeX

@inproceedings{aaai2026_resbenchbenchmar,
  title = {Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input},
  author = {Chenxu Li and Zhicai Wang and Yuan Sheng and Xingyu Zhu and Yanbin Hao and Xiang Wang},
  booktitle = {AAAI 2026},
  year = {2026}
}