Hypernetworks That Evolve Themselves

• URL: http://arxiv.org/abs/2512.16406v1
• Date: Thu, 18 Dec 2025 11:05:34 GMT
• Title: Hypernetworks That Evolve Themselves
• Authors: Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti, Marcello Barylli, Milton Montero, Kathrin Korte, Sebastian Risi,
• Abstract summary: We propose Self-Referential Graph HyperNetworks, systems where variation and inheritance is embedded in the network.<n>By uniting hypernetworks, parameter generation, and graph-based representations, Self-Referential GHNs mutate and evaluate themselves while adapting selectable traits.<n>Our findings support the idea that evolvability itself can emerge from neural self-reference.
• Score: 3.4524024382493774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How can neural networks evolve themselves without relying on external optimizers? We propose Self-Referential Graph HyperNetworks, systems where the very machinery of variation and inheritance is embedded within the network. By uniting hypernetworks, stochastic parameter generation, and graph-based representations, Self-Referential GHNs mutate and evaluate themselves while adapting mutation rates as selectable traits. Through new reinforcement learning benchmarks with environmental shifts (CartPoleSwitch, LunarLander-Switch), Self-Referential GHNs show swift, reliable adaptation and emergent population dynamics. In the locomotion benchmark Ant-v5, they evolve coherent gaits, showing promising fine-tuning capabilities by autonomously decreasing variation in the population to concentrate around promising solutions. Our findings support the idea that evolvability itself can emerge from neural self-reference. Self-Referential GHNs reflect a step toward synthetic systems that more closely mirror biological evolution, offering tools for autonomous, open-ended learning agents.

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