Even with the major advances in Language Modelling in recent years after the introduction of transformer architecture, reasoning is still one of the unique skills of the human brain that Deep Learning models struggle to replicate the most. Since one of the main challenges is to efficiently recall information seen in the past, the Associative Recall (AR) synthetic task has gained importance for being a good proxy for language modelling and a suitable benchmark to select promising Large language models. A series of recurrent-gated models (such as H3, Mamba and Hyena), built to overcome the drawbacks of the O(L^2) computational complexity of the attention module, recently gained popularity for solving AR even with long sequences (more than 10,000 tokens).