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Historical photographs and other canonical images can now be edited seamlessly with a single instruction, often leaving no reliable pixel-level trace. In such cases, the only evidence of manipulation may be a fact about what the image depicts. Existing benchmarks instead rely on generator artefacts, image-caption incon...
Computer-use agents (CUAs) have made rapid progress in completing complex tasks through graphical user interfaces, yet post-training centered on task success alone does not induce reliable safety behavior. A reliable CUA must condition its execution on risk: it should complete ordinary benign tasks, avoid environmental...
This work introduces \methodname{}, an adversarial reinforcement learning framework that pits two heterogeneous models against each other, and shows how a detector trained within this loop improves monotonically across rounds on each of three external benchmarks.
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