🤖 AI Summary
This study addresses the limitations of differentiable feedforward mappings by exploring a novel paradigm for intelligence emergence based on simple computational substrates. Through evolutionary search, we train local recurrent computations in minimal systems such as cellular automata and propose the theory of "Latent Universality," demonstrating that arbitrary partially computable functions can be realized under fixed rules solely by varying initial conditions. Experiments show that models with merely dozens of parameters achieve exact arithmetic extrapolation, behavioral control, and online adaptation. This work validates the feasibility of emergent intelligence within minimal substrates, effectively expanding both the design space and theoretical boundaries of machine learning beyond conventional differentiable architectures.
📝 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.