This paper investigates the in-context learning (ICL) capabilities of Mamba, a recently proposed state space model, and compares it with transformer models. The authors evaluate Mamba on tasks involving simple function approximation and more complex natural language processing (NLP) problems. They find that Mamba performs on par with transformer models in terms of ICL performance across both categories of tasks. Further analysis reveals that Mamba appears to solve ICL problems by incrementally optimizing its internal representations, similar to transformers. The study suggests that Mamba can be an efficient alternative to transformers for ICL tasks involving long input sequences, potentially enabling generalizations of in-context learned AutoML algorithms to longer input sequences. The paper also discusses the broader impact of understanding ICL mechanisms and highlights limitations and future directions for research.This paper investigates the in-context learning (ICL) capabilities of Mamba, a recently proposed state space model, and compares it with transformer models. The authors evaluate Mamba on tasks involving simple function approximation and more complex natural language processing (NLP) problems. They find that Mamba performs on par with transformer models in terms of ICL performance across both categories of tasks. Further analysis reveals that Mamba appears to solve ICL problems by incrementally optimizing its internal representations, similar to transformers. The study suggests that Mamba can be an efficient alternative to transformers for ICL tasks involving long input sequences, potentially enabling generalizations of in-context learned AutoML algorithms to longer input sequences. The paper also discusses the broader impact of understanding ICL mechanisms and highlights limitations and future directions for research.