PCoRT: Progressive Chain-of-Thought Retrieval Tuning for Robust Semi-Supervised Vision

Semi-supervised learning (SSL) typically exploits unlabeled data through pseudo-labeling, but predictions on external or heterogeneous data can be unreliable when their distributions differ from that of the labeled target data. Recent work has introduced Chain-of-Thought (CoT) reasoning to provide additional semantic information for unlabeled visual data. However, existing approaches generally treat a CoT as a single semantic unit, leaving the distinct information conveyed by its intermediate stages underused. We propose Progressive Chain-of-Thought Retrieval Tuning (PCoRT), which preserves the stage-wise structure of CoT and progressively retrieves and fuses stage-specific semantic evidence into visual representations. PCoRT supports two settings with different uses of unlabeled data: in the in-distribution setting, CoT-enhanced predictions refine conventional pseudo-supervision; in the out-of-distribution setting, external unlabeled data provide auxiliary semantic evidence rather than direct pseudo-labels for the target task. We further distill the CoT-enhanced teacher into a visual-only student, which does not require the auxiliary CoT, retrieval, or fusion modules at deployment. Experiments on image classification, object detection, and semantic segmentation across four unlabeled-data settings show that PCoRT achieves competitive performance in all settings, with particularly strong gains in cross-dataset and heterogeneous multi-source settings.