The generation of sustained, open-ended complexity from local interactions remains a fundamental challenge in artificial life. Differentiable multi-agent systems, such as Petri Dish Neural Cellular Automata (PD-NCA), exhibit rich self-organization driven purely by spatial competition; however, they are highly sensitive to hyperparameters and frequently collapse into uninteresting patterns and dynamics, such as frozen equilibria or structureless noise. In this paper, we introduce PBT-NCA, a meta-evolutionary algorithm that evolves a population of PD-NCAs subject to a composite objective that rewards both historical behavioral novelty and contemporary visual diversity. Driven by this continuous evolutionary pressure, PBT-NCA spontaneously generates a plethora of emergent lifelike phenomena over extended horizons-a hallmark of true open-endedness. Strikingly, the substrate autonomously discovers diverse morphological survival and self-organization strategies. We observe highly regular, coordinated periodic waves; spore-like scattering where homogeneous groups eject cell-like clusters to colonize distant territories; and fluid, shape-shifting macro-structures that migrate across the substrate, maintaining stable outer boundaries that enclose highly active interiors. By actively penalizing monocultures and dead states, PBT-NCA sustains a state of effective complexity that is neither globally ordered nor globally random, operating persistently at the "edge of chaos".

翻译：从局部相互作用中生成持续、开放的复杂性，仍是人工生命领域的基本挑战。可微分多智能体系统（如佩特里皿神经细胞自动机PD-NCA）通过纯粹的空间竞争展现出丰富的自组织行为；然而，这类系统对超参数高度敏感，常坍缩为无趣的模式与动态，例如冻结平衡态或无序噪声。本文提出PBT-NCA——一种元进化算法，通过训练PD-NCA种群并施加复合目标——既奖励历史行为新颖性，又奖励当前视觉多样性——推动系统演化。在持续进化压力驱动下，PBT-NCA能自发产生大量涌现性类生命现象（其持续时间远超常规尺度），这正是真正开放式发现的标志性特征。引人注目的是，该基底自主发现了多样的形态生存与自组织策略：我们观察到高度规则的周期性协调波；孢子状散射——同质群体喷射类细胞集群以拓殖远域；以及跨越基底迁移的流变宏观结构——其稳定外边界包裹着高度活跃的内核。通过主动惩罚单文化与死亡状态，PBT-NCA维持了一种有效复杂态：既非全局有序亦非全局随机，持久运行于“混沌边缘”。