Large Language Models (LLMs) are getting smarter, but they're also getting bigger and more computationally expensive. Researchers are exploring new architectures like Mamba, built on State Space Models (SSMs), to make LLMs faster and more efficient. Mamba boasts linear complexity and constant memory, promising performance comparable to attention-based models like Transformers, especially on long sequences. But there's a catch: new research has uncovered that Mamba sometimes takes shortcuts, excelling at tasks with localized key information but struggling when that information is spread out. This inconsistency arises from Mamba's reliance on 'local pattern shortcuts.' Essentially, Mamba gets really good at remembering local patterns within its limited memory capacity, but this hinders its ability to retain more dispersed information. Imagine trying to remember a long list of items by only focusing on small groups—you might miss the bigger picture. To overcome this limitation, researchers have introduced a 'global selection module' to help Mamba see the forest for the trees. This module helps Mamba consider the entire context, not just local patterns, significantly boosting its performance on complex tasks. The results are impressive: with just a small increase in parameters, the enhanced Mamba model shows dramatic improvements on tasks requiring it to process dispersed information. This research has significant implications for the future of LLMs. While efficiency is crucial, it shouldn't come at the cost of accuracy and robustness. By understanding and mitigating these shortcuts, we can build AI models that are both fast and truly intelligent.