ABACUS: A Benchmark for Image Editing Models to Assess Object Category and Counting Under Scene Conservation

arXiv:2610.28316v1 Announce Type: cross Abstract: Instruction-based image editing models can now add, remove a specified number of objects or change their category while preserving the scene. Yet these models often fail to produce the correct number, and these failures grow sharply as scenes become more complex and the requested count increases. Current benchmarks largely miss this problem: they score edits with holistic similarity metrics or with vision-language model (VLM) judges that are themselves poor at counting, so numerical errors go unnoticed or are graded incorrectly. As a result, th

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