Paired-Control Contribution Audits for Diffusion-Assisted Incremental Recognition

Class-incremental learning (CIL) and its few-shot variant (FSCIL) train a classifier on classes that arrive over time while it must keep recognizing earlier ones. Recent methods insert diffusion models into this pipeline, for example to generate replay images of earlier classes, to select among generated samples, or to supply frozen features. When such a method gains accuracy, the gain may come from the generated samples, from simply training on more data, from the selection step, from a stronger representation, or from extra training steps, and final accuracy alone cannot tell these apart. We propose a paired-control contribution audit, an evaluation protocol that tests which component supports such a gain. For each way a method uses diffusion, we rerun the same method with only that use replaced by a simple substitute, for example random instead of reward-based selection, while keeping the data, seeds, and training steps fixed, and we report the accuracy difference over repeated matched runs. We apply the audit to six diffusion-assisted CIL and FSCIL image-classification methods and check each conclusion against alternative substitutes. Each comparison shows what supports a gain only in the tested setting, and the answers differ across methods. Diffusion-generated image replay in one method raises average incremental accuracy by up to 10.1 points, and the gain is not explained by extra training steps, whereas in three other methods the diffusion component shows no reliable gain over a simpler substitute, such as an unselected pool of generated images or replay of stored class statistics. We therefore recommend reporting, alongside final accuracy, how each diffusion component compares with its simplest substitute and the settings in which each claim was tested. Code is available at https://anonymous.4open.science/r/dca-iclr27-F078.