Sequence models stop squeezing history into one state: semantic similarity picks the lag, 95.35% on IMDB
A sequence-modeling framework written as a nonlinear time-delay system lets the current input's semantic similarity to past inputs set how far back the model looks, reaching 95.35% accuracy on IMDB sentiment classification with 2.6M parameters, above the 89.90% of LSTM and 83.00% of Mamba in the same table.