arXiv:2602.22428v2 Announce Type: replace Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing approaches, however, focus on maximizing reward rather than sampling from the true Bayesian posterior, leading to miscalibrated inference. In this work, we show that common test-time guidance methods do not recover the correct posterior distribution and identify the structural approximations responsible for this failure. We then propose consistent alternative estimators that enable calibrated sampling
The rapid advancement and widespread deployment of diffusion models necessitate better calibration methods to ensure reliable and unbiased AI outputs.
This research addresses a fundamental limitation in current generative AI, improving the trustworthiness and scientific rigor of AI-generated content and inferred distributions.
The understanding and application of test-time guidance for diffusion models will shift from maximizing reward to achieving statistically sound, calibrated Bayesian inference, leading to more accurate and reliable AI systems.
- · AI researchers
- · Developers of diffusion models
- · Sectors relying on generative AI for critical applications
- · Systems relying on miscalibrated diffusion models
- · Methods that prioritize reward maximization over statistical correctness
More accurate and reliable generative AI outputs, particularly in scientific and engineering domains.
Increased trust and broader adoption of diffusion models in sensitive applications where statistical correctness is paramount.
New frameworks for AI safety and interpretability that build upon provably calibrated generative processes.
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Read at arXiv cs.LG