AI RESEARCH
Generative Inverse Design with Abstention via Diagonal Flow Matching
arXiv CS.LG
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ArXi:2603.15925v1 Announce Type: new Inverse design aims to find design parameters $x$ achieving target performance $y^*$. Generative approaches learn bidirectional mappings between designs and labels, enabling diverse solution sampling. However, standard conditional flow matching (CFM), when adapted to inverse problems by pairing labels with design parameters, exhibits strong sensitivity to their arbitrary ordering and scaling, leading to unstable