Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems

This work challenges the assumption that compositional reasoning emerges as a byproduct of symbol grounding in neuro-symbolic AI. It introduces the $i$LTN architecture, demonstrating that models trained solely on a grounding objective fail to generalize, while joint training on perceptual grounding and multi-step reasoning is crucial.