This paper presents Neuroevolution Arena, a GPU-accelerated spatial ecology for evaluating independently parameterized neural-network cells, using an audit-tracked nested evaluation protocol. Three implementation-specific update-and-inheritance regimes (EvoEvo, EvoRL, and RLRL) are crossed with two neural architectures for 50,000 generations in three independent training runs per condition. One saved elite-controller artifact from each of the 18 runs enters an aligned-run frozen-evaluation design comprising 198 computational jobs. The results show that RL-enabled regimes attain higher recorded training fitness than EvoEvo, whereas pairwise outcomes exhibit architecture-conditioned majority patterns and substantial artifact dependence. Six-way winners vary across artifacts and contexts, and the prespecified survival endpoint has a complete floor. We contribute a nested protocol that separates training-run artifacts from evaluation contexts and exposes, rather than conceals, their different sources of variation. Blogger's Review: This paper proposes an innovative neuroevolution evaluation framework that can deeply investigate the performance of different update and inheritance mechanisms in neural architectures, with significant theoretical and practical implications.