Native Symbolic Emergence Should Replace the Translation Layer Paradigm in Neuro-Symbolic AI

The dominant paradigm in neurosymbolic AI assumes that symbolic structure must be defined before learning or extracted after it. We identify this assumption as an unnecessary architectural constraint, and we call it the symbolization gap. This position paper argues that symbolic representations should emerge naturally from neuromorphic learning dynamics without gradient descent, a translation layer, or prior vocabulary knowledge. To instantiate our position, we introduce NSSN (Neuromorphic Symbolic Spiking Network). In NSSN, each Spike-Timing-Dependent Plasticity (STDP) co-activation directly produces a new symbolic node. Four provable properties hold for any finite corpus, any domain alphabet, and any training order: monotonic growth, convergence, incremental preservation, and subquadratic complexity. Two experiments confirm that these properties hold on real data across qualitatively distinct domains. On the Sepsis Cases dataset, 13 clinical sequences are recovered with less than 0.05% variance across three training orders. On Fashion-MNIST, the same STDP rule produces a qualitatively different topology in spatial mode. The space of architectures enabling native symbolic emergence has been systematically underexplored. NSSN provides the formal and empirical foundations to explore it.