arXiv:2510.00399v2 Announce Type: replace Abstract: The Mamba model has gained significant attention for its computational advantages over Transformer-based models, while achieving comparable performance across a wide range of language tasks. Like Transformers, Mamba exhibits in-context learning (ICL) capabilities, i.e., making predictions for new tasks based on a prompt containing input-label pairs and a query, without requiring fine-tuning. Despite its empirical success, the theoretical understanding of Mamba remains limited, largely due to the nonlinearity introduced by its gating mechanism
The paper provides a theoretical understanding of Mamba's in-context learning capabilities, which is timely given Mamba's recent emergence as a computationally efficient alternative to Transformers.
A deeper theoretical understanding of Mamba's ICL capabilities could accelerate its development and deployment, potentially leading to more efficient and powerful AI models for a variety of tasks.
The theoretical proof of Mamba's ability to learn in context, even with outliers, strengthens its position as a viable and potentially superior alternative to Transformer models in certain applications.
- · AI researchers
- · Mamba model developers
- · Organizations seeking efficient AI deployments
Increased research and development efforts into Mamba-based AI architectures.
Broader adoption of Mamba-based models in AI applications due to proven efficiency and ICL capabilities.
Potential for Mamba to become a dominant architecture in specific areas of AI, challenging Transformer's ubiquity.
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Read at arXiv cs.LG