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MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large language models (MLLMs). Existing benchmarks largely assess semantic comparison or single-image grounding in isolation, without jointly requiring faithful description and physical localization. To bridge this gap, we introduce MinCU, a benchmark for grounded minimal-change understanding, where each sample consists of an image pair differing by a single atomic variation in object category, attribute, count, or spatial position, and models are evaluated

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