Saving Foundation Flow-Matching Priors for Inverse Problems

• URL: http://arxiv.org/abs/2511.16520v1
• Date: Thu, 20 Nov 2025 16:35:57 GMT
• Title: Saving Foundation Flow-Matching Priors for Inverse Problems
• Authors: Yuxiang Wan, Ryan Devera, Wenjie Zhang, Ju Sun,
• Abstract summary: Foundation flow-matching (FM) models promise a universal prior for solving inverse problems (IPs)<n>We introduce FMPlug, a plug-in framework that redefines how foundation FMs are used in IPs.<n>Our results point to a path for making foundation FM models practical, reusable priors for IP solving.
• Score: 9.257036746938422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation flow-matching (FM) models promise a universal prior for solving inverse problems (IPs), yet today they trail behind domain-specific or even untrained priors. How can we unlock their potential? We introduce FMPlug, a plug-in framework that redefines how foundation FMs are used in IPs. FMPlug combines an instance-guided, time-dependent warm-start strategy with a sharp Gaussianity regularization, adding problem-specific guidance while preserving the Gaussian structures. This leads to a significant performance boost across image restoration and scientific IPs. Our results point to a path for making foundation FM models practical, reusable priors for IP solving.

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