AI 中文总结
研究受波粒行走者启发为自主物理计算开发闭包准则，将行走者表述为频闪储层，定义计算为稳健的粗粒度转移保持，指出自主物理计算需进一步闭包条件，分类波粒行走者并转化区别为设计原则。
AI 中文摘要
物理储层、神经形态器件和波介导系统通常具有记忆、反馈和丰富的状态依赖动力学，但这些特性本身并不构成自主计算。在此，我们受波粒行走者启发，为自主物理计算开发了一种闭包准则。我们将行走者表述为具有状态的频闪储层，其中波场存储先前液滴撞击的指数衰减痕迹，并通过局部斜率耦合引导未来运动。该模型分离了物理写入、存储、读取、反馈和外部触发擦除。然后我们将计算定义为稳健的粗粒度转移保持：只有当粗粒度满足组合性时，物理映射才实现抽象转移，抽象状态由分离的物理盆地实现，转移在噪声下稳定。自主物理计算需要进一步的闭包条件：内部物理读出状态必须选择下一个物理操作。此准则将波粒行走者分类为具有真正类似图灵原语的波记忆机器，但不是封闭的自主物理计算机，因为擦除相移是外部施加的。该框架将这种区别转化为设计原则：当物理读出盆地与操作选择耦合时，记忆就变成了自主计算。
英文摘要
Physical reservoirs, neuromorphic devices, and wave-mediated systems often possess memory, feedback, and rich state-dependent dynamics, but these properties do not by themselves establish autonomous computation. Here we develop a closure criterion for autonomous physical computation, motivated by the wave--particle walker. We formulate the walker as a stroboscopic reservoir with state, where the wave field stores an exponentially decaying trace of previous droplet impacts and guides future motion through local slope coupling. This model separates physical writing, storage, reading, feedback, and externally triggered erasure. We then define computation as robust coarse-grained transition preservation: a physical map implements an abstract transition only when a coarse-graining satisfies compositionality, with abstract states realized by separated physical basins and transitions stable under noise. Autonomous physical computation requires a further closure condition: an internal physical readout state must select the next physical operation. This criterion classifies the wave--particle walker as a wave-memory machine with genuine Turing-like primitives, but not as a closed autonomous physical computer, because the erasing phase shift is externally imposed. The framework turns this distinction into a design principle: memory becomes autonomous computation when physical readout basins are coupled back to operation selection.
