发表机构
Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过分离训练与执行随机性，发现异步更新提升NCA学习可靠性，但确定性执行下持续与再生模型更稳定，随机性作用取决于任务阶段。
AI 中文摘要
随机细胞更新通常在整个神经细胞自动机（NCA）的生命周期中被使用，从时间反向传播到最终展开。这留下了两个纠缠的问题：更新随机性是否有助于学习有用的规则，以及这种随机性在执行时是否必须保留？我们在受控的Growing NCA实验中分离了训练和评估更新模式，然后改变训练期间显示的状态。在标准的恒定速率持续（persist）配方下，异步训练在10/10次运行中通过了短视距质量测试，而同步训练为3/10次。所有十个异步模型在确定性评估下也保留了目标4,096步。对于标量平移不变晶格，我们推导出一个精确的均方准则：随机掩蔽可以抑制均值模式，但它也注入方差，并且仅均值的测试错误分类了四个非边缘设置。最后，在30个都通过相同重建测试的模型中，十个生长训练模型中有八个在4,096步时偏离目标，而所有持续和再生（regenerate）模型都保留了目标；损伤恢复将持续与再生区分开来。结果区分了优化可靠性、执行模式和任务特定行为，而不是将它们视为一个稳定性属性。
英文摘要
Stochastic cell updates are often used throughout the life of a neural cellular automaton (NCA), from backpropagation through time to final rollout. This leaves two questions entangled: does update randomness help learn a useful rule, and must that randomness remain at execution? We separate training and evaluation update modes in controlled Growing NCA experiments, then vary the states shown during training. Under the standard constant-rate persist recipe, asynchronous training passes the short-horizon quality test in 10/10 runs, compared with 3/10 synchronous runs. All ten asynchronous models also retain the target for 4,096 steps under deterministic evaluation. For a scalar translation-invariant lattice, we derive an exact mean-square criterion: random masking can damp mean modes, but it also injects variance, and a mean-only test misclassifies four non-marginal settings. Finally, among 30 models that all pass the same reconstruction test, eight of ten grow-trained models become off-target at 4,096 steps, while all persist and regenerate models retain the target; damage recovery separates persist from regenerate. The results distinguish optimization reliability, execution mode, and task-specific behavior instead of treating them as one stability property.
Comments21 pages, 7 figures
