Breaking Majority Dominance: Progressive Abductive Learning under Concept Skewness

Emulating the human interplay between perception and reasoning, abductive learning (ABL) is proposed to improve the perception model's ability in predicting concept labels without explicit annotations by drawing knowledge-consistent supervision signals from logical abduction, thereby benefiting reasoning. However, in practice, concept distributions are often skewed, leading to a biased feedback loop in ABL: early predictions favor majority concepts, making majority-dominated abduction candidates more likely to be reused as pseudo-labels to supervise the update of the perception model, further amplifying the bias against minority concepts. To solve this problem, we propose distribution-prior-rectified progressive abductive learning (DPRP-ABL). DPRP-ABL limits ambiguous candidate competition early in training by progressively expanding nested knowledge sub-bases. Within each active candidate set, it uses estimated concept distribution priors to alleviate majority-favoring scoring bias. Our analysis characterizes how these components affect candidate selection at a fixed model state and establishes conditions for preserving ground-truth feasibility across progressive phases. Experiments on two typical tasks indicate that DPRP-ABL improves reasoning-output performance and concept recognition under concept skewness.