Transformers have a quadratic attention complexity, meaning that as the input sequence length increases, the computational requirements grow quadratically. This poses challenges for processing infinitely long inputs because the memory and computational resources needed become impractical. Additionally, transformers face difficulties in retaining information beyond the attention window, which can result in forgetting relevant context. Long-context processing also presents challenges, as the model may struggle to maintain coherence and relevance over extended sequences. These limitations highlight the need for innovative solutions to address scalability and long-context processing in transformer-based architectures.