AI RESEARCH
Collapse-Free Prototype Readout Layer for Transformer Encoders
arXiv CS.LG
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ArXi:2604.03850v1 Announce Type: new DDCL-Attention is a prototype-based readout layer for transformer encoders that replaces simple pooling methods, such as mean pooling or class tokens, with a learned compression mechanism. It uses a small set of global prototype vectors and assigns tokens to them through soft probabilistic matching, producing compact token summaries at linear complexity in sequence length. The method offers three main advantages. First, it avoids prototype collapse through an exact decomposition of the.