Attention Is Not What You Need: Grassmann Flows as an Attention-Free Alternative for Sequence Modeling ZHANG CHONG Self-attention has become the de facto primitive for modern sequence models. It is often implicitly assumed that strong natural language performance requires attending over all token pairs via a dense or approximate attention mechanism. In this paper we question that assumption. We reinterpret self-attention as a particular instance of tensor lifting: a hidden vector is mapped into
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