A practical implementation of this idea is a variation of the mixture of experts architecture (MOE, Fig. ). The basic version of the MOE architecture is to combine several distinct models (Fig. , i.e., the titular experts) into a combined network with a gating mechanism that chooses We recently showed how such an architecture facilitates the prediction of chemical structures from different spectral sources. For example, models that map individual spectra (e.g., 1H-NMR, IR, and EI-MS) to SMILES strings can be straightforwardly combined by weighting their next-token predictions and recursively predicting on a shared string (e.g., using a transformer architecture). During inference, such models pass control back and forth between the different spectral sources in a manner that often mimics expert deduction. For example, the mass spectrometry model tends to predict the end token for the string, whereas the 1H-NMR tends to dominate the prediction of alkylic segments. This architecture is also surprisingly robust to variable and even contradictory information. For example, when trained with random dropping of spectral inputs, it nearly matches the performance of analogous fixed-input models and still predicts the product suggested by the majority of evidence when contradictory inputs are supplied. This is just one example and one set of architectural choices. The essential components of this approach are to recursively decompose the calculation and to expand the notion of useful training curricula. In the structure prediction example, the calculation was decomposed into token-by-token prediction of a SMILES string, but there is nothing stopping this approach from being applied to generic latent-space compressions. that dedicated to human instruction will likely be the focus of future efforts along this frontier if the goal is to mimic human deduction.