arXiv:2606.02331v1 Announce Type: cross Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supported by the measurement. We study this failure as measurement-conditioned hallucination: visually meaningful content that is either implausible or inconsistent with the measured instance. Our analysis separates Bayes-rule-based diffusion inverse solvers into a prior update and a measurement-conditioning step, showing that hallucinated content can enter through the prior-side proposal before the mea
The rapid advancement and deployment of generative AI models highlight the increasing need for robust and reliable outputs, especially in critical applications.
Addressing hallucinations in diffusion models is crucial for their trustworthy integration into scientific and industrial inverse problems, impacting areas from medical imaging to engineering.
The focus on 'hallucination-aware' sampling marks a refinement in AI development, prioritizing accuracy and consistency with real-world data over mere realism in generative outputs.
- · AI safety researchers
- · Medical imaging
- · Scientific research
- · Generative AI developers
- · Untrustworthy AI applications
- · Models prioritizing aesthetics over accuracy
Improved reliability and explainability of diffusion-based inverse problem solvers.
Accelerated adoption of AI in sensitive applications requiring high fidelity and data consistency.
Increased public and institutional trust in AI, potentially leading to broader regulatory frameworks focusing on model integrity.
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Read at arXiv cs.LG