Certified Conflict Policies for Few-Shot Class Registration

Deployed vision systems often need to recognize new categories from only a few labeled examples while keeping the existing classifier fixed. However, freezing the classifier does not freeze its predictions. New classes compete with old classes in the same decision rule and can cause old examples to be classified as new. We study this problem as few-shot class registration and introduce Certified Arbitration for Post-training Expansion (CAPE), which provides finite-sample control of old-to-new interference over an old-class calibration frame. CAPE separates risk certification from conflict ranking, using a scorer to prioritize new-class predictions that are less likely to interfere with old classes. We find that certified utility depends jointly on calibration evidence and ranking quality, motivating Certified Conflict Policy Learning (CCPL), which learns a reusable scorer across registrations and applies it without refitting. Across 900 development episodes under a common certification protocol, CCPL retains substantially more new-class utility than two external scoring pipelines. CCPL also improves over the episode-local CAPE baseline and transfers without retraining to both held-out populations, including a 3.27 percentage-point gain on FGVC Aircraft. These results show that reusable conflict scoring can improve utility under finite-sample risk control for class registration without retraining the old classifier.