NeuronSoup用遗传算法进化共享神经元图，不依赖反向传播，在MNIST上达到85.9%准确率，模型只有115KB，适合探索非梯度训练的新路子。
NeuronSoup是一种新型神经计算架构，用异步延迟介导的信号传播取代逐层同步处理，隐藏神经元在路径间共享。在10类MNIST数字分类任务中，使用冻结ResNet18特征输入，系统进化出204条活跃路径、266个隐藏神经元（156个共享，1个参与11条路径），10,000代后测试准确率85.9%，模型仅115KB。该架构无需可微计算图，每个样本自适应计算深度，并自动发现处理路径间的横向交互。
NeuronSoup: Evolving Asynchronous, Shared-Neuron Temporal Graphs without Backpropagation
We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of shared neurons. Each path in the network routes a continuous-valued signal from one input neuron to one output neuron through a variable number of intermediate hidden neurons. Hidden neurons are physically shared across paths: when two paths pass through the same neuron, the second arrival encounters the accumulated state left by the first, producing constructive or destructive interference that depends on signal polarity and arrival timing. The entire architecture -- topology, weights, delays, and connectivity -- is co-evolved by a genetic algorithm operating on a flat real-valued genome of 14,602 genes. On 10-class MNIST digit classification using frozen ResNet18 features as input, the system evolves a network of 204 active paths through 266 hidden neurons (156 shared across multiple paths, with one neuron participating in 11 distinct paths) and achieves 85.9\% test accuracy after 10,000 generations. The trained model occupies 115 KB. We argue that this architecture addresses fundamental limitations of current deep learning: it requires no differentiable computation graph, adapts its computation depth per-sample, and discovers lateral interactions between processing pathways that current architectures must engineer explicitly. We discuss why genetic algorithms are the correct optimization tool for this problem class, why CMA-ES fails at this scale, and how the architecture generalizes to arbitrary domains by substituting the encoder and output structure.