This work investigates the emergence and computational role of oscillatory phase coding (OPC) in neural systems. We trained adaptive recurrent neural networks (RNNs) on sequence working‑memory tasks with variable delays. After training, OPC arose spontaneously: memorized items reappear at distinct phases of a shared rhythm, with oscillation amplitude encoding item identity and phase encoding serial order.\n\nAblation studies identified three essential factors for OPC: adaptation in neural dynamics, variable delay during memory retention, and flexible readout of information. Neuron‑type analysis revealed the coexistence of content cells and time cells; the former process item identity, the latter handle temporal order, operating largely independently.\n\nRecurrent connectivity analysis showed phase‑organized connections among time cells and structured, digit‑selective connections within content‑cell groups. Inspired by these findings, we proposed a neural circuit model in which interactions between content units and an adaptive time ring reproduce OPC.\n\nThe study demonstrates how network architecture and task demands give rise to OPC, offering new insights into its implementation in biological brains and guiding the development of computational models with human‑like cognitive abilities.\n\nReview: The paper provides a clear mechanistic account of how adaptive RNNs can develop phase‑based coding, bridging theoretical neuroscience and machine learning in a compelling way.