TinyCeNN-LM proposes a quality-gated conversion framework for replacing attention in pretrained language models with CeNN-inspired cellular-recurrent layers. This approach has implications for the development and deployment of AI agents, as it allows for more efficient and flexible model architectures. The results show that the proposed method maintains representation fidelity and improves performance in certain scenarios.