title: KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
summary: 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.
paper_authors._id: 682fdd77e3102e71872d9b00
paper_authors.name: Yongliang Wu
paper_authors._id: 682fdd77e3102e71872d9b01
paper_authors.name: Zonghui Li
paper_authors._id: 682fdd77e3102e71872d9b02
paper_authors.name: Xinting Hu
paper_authors._id: 682fdd77e3102e71872d9b03
paper_authors.name: Xinyu Ye
paper_authors._id: 682fdd77e3102e71872d9b04
paper_authors.name: Xianfang Zeng
paper_authors._id: 682fdd77e3102e71872d9b05
paper_authors.name: Gang Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:17.710Z
paper_authors._id: 63417332c5565a4b8d43a0d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 682fdd77e3102e71872d9b06
paper_authors.name: Wenbo Zhu
paper_authors._id: 682fdd77e3102e71872d9b07
paper_authors.name: Bernt Schiele
paper_authors._id: 682fdd77e3102e71872d9b08
paper_authors.name: Ming-Hsuan Yang
paper_authors._id: 682fdd77e3102e71872d9b09
paper_authors.name: Xu Yang
paper_title: KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
paper_summary: 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.
paper_ai_summary: KRIS-Bench assesses generative models' knowledge-based reasoning in image editing through a taxonomy of editing tasks and a Knowledge Plausibility metric.
paper_ai_keywords: multi-modal generative models instruction-based image editing knowledge-based reasoning KRIS-Bench cognitive assessment foundational knowledge types Factual Conceptual Procedural reasoning dimensions Knowledge Plausibility metric