AINeutralarXiv – CS AI · Mar 26/1015
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Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures
Researchers conducted an in-depth analysis of in-context learning capabilities across different AI architectures including transformers, state-space models, and hybrid systems. The study reveals that while these models perform similarly on tasks, their internal mechanisms differ significantly, with function vectors playing key roles in self-attention and Mamba layers.