[2511.17038] DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing

Computer Science > Artificial Intelligence arXiv:2511.17038 (cs) [Submitted on 21 Nov 2025 (v1), last revised 19 Mar 2026 (this version, v2)] Title:DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing Authors:Hao Chen, Renzheng Zhang, Scott S. Howard View a PDF of the paper titled DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing, by Hao Chen and 2 other authors View PDF HTML (experimental) Abstract:From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process. However, this formulation fails to explain its practical behavior: the prior offers limited guidance, while reconstruction is largely driven by the measurement-consistency term, leading to an inference process that is effectively decoupled from the diffusion dynamics. We show that the diffusion prior in these solvers functions primarily as a warm initializer that places estimates near the data manifold, while reconstruction is driven almost entirely by measurement consistency. Based on this observation, we introduce \textbf{DAPS++}, which fully decouples diffusion-based initialization from likelihood-driven refinement, allowing the likelihood term to guide inference more directly while maintaining numerical stability and providing insight into why unified diffusion trajectories remain effective in practice. By requiring fewer function evaluations (NFEs...