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Object hallucination in LVLMs can be significantly reduced *after* training, without any extra data or compute.
Finetuning visual foundation models with LoRA-based pairwise training dramatically improves AIGI detection robustness against real-world distortions.
Even state-of-the-art AI-generated image detectors struggle when images are cropped, resized, or compressed, revealing a critical gap in real-world robustness.
