Graph Memory Transformer replaces FFN sublayers with a graph memory cell using 128 centroids and transition matrices per block, yielding stable training at 82.2M parameters but higher validation loss than a 103M dense baseline.
MemoryFormer: Minimize transformer computation by removing fully-connected layers
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Deeper transformer layers benefit from context-free token-specific value vectors in a Bank of Values lookup table, improving performance over standard attention with less compute.
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Do Value Vectors in Deep Layers Need Context from the Residual Stream?
Deeper transformer layers benefit from context-free token-specific value vectors in a Bank of Values lookup table, improving performance over standard attention with less compute.