MOSAIC: Mode-Aware Prototypical NeuroSymbolic AI for Concept Learning

NeuroSymbolic (NeSy) predictors combine the expressive power of neural networks with the safety and interpretability of symbolic methods by reasoning over learned intermediate concepts. Yet state-of-the-art methods remain vulnerable to reasoning shortcuts (RSs): incorrect input-to-concept mappings that nevertheless achieve high task accuracy under distant supervision. Many mitigation strategies require dense concept annotations, while recent prototypical approaches avoid both shortcuts and dense supervision by grounding each concept in a single prototype. However, they implicitly assume that each concept induces a single-mode distribution in the embedding space. In this paper, we show that the unimodality assumption gives rise to previously overlooked RSs at the latent-mode level, which we term implicit reasoning shortcuts (IRSs), in contrast to explicit reasoning shortcuts (ERSs) operating at the concept level. We prove that mode-blind predictors are structurally susceptible to IRSs: their optima hurts concept accuracy by conflating distinct modes and this ambiguity persists even with full concept supervision and complete data coverage. To address this limitation, we introduce $\text{M}$ode-Aware Pr$\text{O}$totypical Neuro$\text{S}$ymbolic $\text{AI}$ for $\text{C}$oncept Learning ($\text{MOSAIC}$), which discovers latent concept modes from unlabelled support data using infinite mixtures. We derive its embedding updates, characterise its deterministic optima, and establish, through an identifiability analysis, that optimal mode-aware predictors recover the intended concept semantics up to a permutation of latent modes. Empirically, MOSAIC substantially improves concept accuracy over existing reasoning-shortcut mitigation methods on the new Omniglot-EvenOdd benchmark and on the real-world, safety-critical BDDOIA task.