AI RESEARCH
On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods
arXiv CS.LG
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ArXi:2605.13146v1 Announce Type: cross Artificial intelligence (AI) has transformed imaging inverse problems, from medical diagnostics to Earth observation. Yet deep neural networks can produce hallucinations, realistic-looking but incorrect details, undermining their reliability, especially when ground truth data is unavailable. We develop a theoretical framework showing that such hallucinations are not merely artifacts of particular models, but can arise from the ill-posed nature of the inverse problem itself.