Recently, Transformers (e.g., Audio Spectrogram Transformers, AST) and state-space models (e.g., Audio Mamba, AuM) have achieved remarkable progress in audio modeling. However, the O(L^2) computational complexity of the Transformer architecture hinders efficient long-sequence processing, while the Mamba architecture tends to become unstable when scaling parameters and data. To address these challenges, this paper proposes AudioRWKV (A-RWKV), a highly efficient and stable architecture for audio modeling. Specifically, we inherit the stable and efficient recurrent formulation of RWKV7 and replace its 1D token-shift operation with a 2D depthwise separable convolution to better capture local spectro-temporal patterns. Furthermore, we adapt the original causal WKV kernel into a bidirectional WKV kernel (Bi-WKV), enabling global context modeling over the entire audio sequence while maintaining linear computational complexity. Benefiting from the inherent stability of the RWKV7 foundation, A-RWKV scales seamlessly to larger model sizes. Experimental results demonstrate that, under the same linear-model regime, A-RWKV-S (22M) achieves performance parity with AuM-B (92M) while exhibiting more stable throughput than AST; for long-form audio (~5 minutes 28 seconds), WKV7 achieves up to a 13.3X speedup in processing.