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CC-Bench: A Cognitive Conflict Benchmark for MLLMs in Safety-Critical Visual Inspection CC-Bench is a joint medical-industrial benchmark for evaluating whether multimodal large language models (MLLMs) remain visually grounded when plausible textual context conflicts with image evidence. The benchmark reorganizes public anomaly datasets into a unified four-way multiple-choice QA format for high-risk visual inspection. This repository currently contains: 4,282 images in total 2,157… See the full description on the dataset page: https://huggingface.co/datasets/annoymous-1/CC-Bench.