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.