Emergent Models: Intelligence from Tiny Substrates

• URL: http://arxiv.org/abs/2608.14019v1
• Date: Fri, 14 Aug 2026 07:07:05 GMT
• Title: Emergent Models: Intelligence from Tiny Substrates
• Abstract summary: Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates.<n>We show that local-recursive computation at a tiny scale can extrapolate exactly on simple arithmetic functions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, and therefore extrapolating beyond the training range. Theoretically, we prove that some EMs are latent-universal: with the update rule and interface held fixed, they can realize any partial computable function by varying only the initial condition of the latent state. Empirically, we study a zoo of minimal EM instantiations across discrete and continuous substrates, showing that local-recursive computation at a tiny scale (tens to hundreds of parameters) can extrapolate exactly on simple arithmetic functions, can support control behaviour and online adaptation, while still exposing several limitations. This work is foundational: it does not propose a competitive architecture, but a framework meant to widen the design space of machine learning beyond differentiable feed-forward maps.

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