The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguishable from real data. However, as their perceptual quality continues toimprove, these models also exhibit agrowing tendencytogenerate hallucinations -realistic-looking details that do not exist in the ground truth images. Hallucinations in these models create uncertainty about their reliability, raising major concerns about their practical application.

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Recent works have shown that MFLD globally minimizes an entropy-regularized convex functional in the space of measures.

Sequence-to-Sequence (Seq2Seq) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language generation tasks. However,the black-box nature of these models limits their application in tasks where specific rules (e.g., controllable constraints, prior knowledge) need to be executed.

Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function.