Recent research has shed light on the behavior of large language models (LLMs), particularly their tendency to focus excessively on the first token in a sequence, a phenomenon termed 'attention sink.' This behavior can hinder the effective mixing of information between tokens, potentially leading to representational collapse in deep Transformer architectures. A new paper proposes a Multi-Token Attention (MTA) method, which employs convolution operations over multiple query and key vectors to enhance context relevance identification. This approach reportedly outperforms traditional Transformer models in language modeling and long-context tasks. The findings suggest that conventional attention mechanisms struggle to locate context based on multiple criteria, as they rely solely on single query-key vector similarities. The research indicates a need for improved architectural strategies to manage large context lengths effectively, with implications for the development of future LLMs.