Tag
ReCBM proposes an uncertainty-gated relational reasoning framework for Concept Bottleneck Models, introducing concept relations like co-occurrence, implication, and exclusion to recover unreliable or missing concept states and improve interpretability and downstream predictions.
This paper introduces SPARK, a self-play reinforcement learning framework that leverages knowledge graphs derived from scientific literature to improve relational reasoning in vision-language models.