In June 2037, a four-author team from the Independent Research Collective (Ålife Working Group) and the University of Texas at Austin published a small-scale but conceptually significant synthesis in the Journal of Artificial Life and Adaptive Systems: grafting a simplified computational model of Physarum polycephalum (acellular slime mold) tube-reinforcement decision dynamics onto the internal state update rules of Lenia-class artificial organisms.
The Synthesis
The paper’s premise was that two previously separate research threads — Lenia-style continuous cellular automata producing emergent morphologies, and Physarum-style decentralized optimization producing emergent decision-making — could be composed into a single system exhibiting adaptive behavior neither substrate produced alone. The resulting simulated agents demonstrated directed, adaptive foraging behavior in variable-resource environments, a capability absent from baseline Lenia morphologies of comparable parameter complexity.
The paper was explicitly modest in its claims. The authors stated plainly that “readers expecting a bridge to general intelligence will be disappointed by the modesty of what follows.” They framed the result not as a step toward AGI but as “a small existence proof for a broader research direction — building simulated cognitive systems from the bottom up, out of substrates independently validated against biological decision-making and biological morphogenesis, rather than top-down from statistical pattern-matching over symbolic data.”
Connection to Prior Work
The paper’s framework extends the substrate-independent cognition thesis advanced by Sorokina et al. (2026) at the Novosibirsk Institute of Cytology and Genetics. Sorokina’s study had proposed — through a comparison between human cortical organoids and Physarum polycephalum — that the behavioural criteria for adaptive cognition (learning, generalisation, memory) may be substrate-independent. The Okwuosa et al. paper operationalizes that thesis: rather than comparing two biological substrates, it composes a computational model of one (Physarum decision dynamics) with a computational model of another (Lenia morphogenesis) and tests whether the composition produces adaptive behaviour.
The paper also connects to the post-OpenAI research diaspora documented in the WIRED bankruptcy report (2031), which noted researchers heading toward “connectomics labs, artificial life research groups, and a handful of underfunded organoid computing startups.” The Ålife Working Group — unfunded, independent, publishing outside the dominant transformer paradigm — represents precisely the kind of research formation that the diaspora enabled.
Institutional Context
The paper received no external grant support and no institutional press coverage at time of publication. The authors released their simulation framework and parameter sets under an open license, “in the hope that a small community of similarly-inclined researchers might find use in extending it.” This open-release model — common in the independent-collective research ecosystem that emerged after the 2031 industry correction — stands in contrast to the proprietary model of the pre-correction commercial AI sector.
Future Work and the 2045 Connection
The paper’s closing explicitly named “composition with connectomic constraint data” as “a natural and currently unexplored next step.” This is precisely the synthesis that Fennimore et al. (2045) would deliver eight years later at Cognitive Genesis Research: the neural core architecture synthesizing connectomic mapping, slime mold decision substrates, and cortical organoid electrophysiology into a single neuromorphic platform.
The paper’s first author group included Dr. Amara Fennimore, then a visiting researcher at UT Austin Department of Biology, who would co-found Cognitive Genesis Research in approximately 2045. An editorial note appended to the paper observes, without particular emphasis, that this was the first joint publication between the Ålife Working Group and Fennimore.