Emergent Models: Intelligence from Tiny Substrates
Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, within simple dynamical systems, of computational behaviors that solve external tasks. Such substrates typically iterate a fixed local rule over a latent space for an adaptive number of steps, with an interface linking the latent state to external input/output signals. Training proceeds by evolutionary search. We hypothesize that some instances of this framework are biased toward global generalization: capturing the rule generating the data over its full domain, a...
| Authors | Giacomo Bocchese, Nicola Giacobbo, Etienne Guichard et al. |
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| Primary category | cs.NE |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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