  The unfolding of detector effects in experimental data is critical for
enabling precision measurements in high-energy physics. However, traditional
unfolding methods face challenges in scalability, flexibility, and dependence
on simulations. We introduce a novel unfolding approach using conditional
Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM
for a non-iterative, flexible posterior sampling approach, which exhibits a
strong inductive bias that allows it to generalize to unseen physics processes
without explicitly assuming the underlying distribution. We test our approach
by training a single cDDPM to perform multidimensional particle-wise unfolding
for a variety of physics processes, including those not seen during training.
Our results highlight the potential of this method as a step towards a
"universal" unfolding tool that reduces dependence on truth-level assumptions.
