KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
Authors: name: Yongliang Wu, name: Zonghui Li, name: Xinting Hu, name: Xinyu Ye, name: Xianfang Zeng, name: Gang Yu, name: Wenbo Zhu, name: Bernt Schiele, name: Ming-Hsuan Yang, name: Xu Yang

Recent advances in multi-modal generative models have enabled significant
progress in instruction-based image editing. However, while these models
produce visually plausible outputs, their capacity for knowledge-based
reasoning editing tasks remains under-explored. In this paper, we introduce
KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a
diagnostic benchmark designed to assess models through a cognitively informed
lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks
across three foundational knowledge types: Factual, Conceptual, and Procedural.
Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning
dimensions and release 1,267 high-quality annotated editing instances. To
support fine-grained evaluation, we propose a comprehensive protocol that
incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints
and calibrated through human studies. Empirical results on 10 state-of-the-art
models reveal significant gaps in reasoning performance, highlighting the need
for knowledge-centric benchmarks to advance the development of intelligent
image editing systems.