Graph Memory Transformer (GMT) 事件

Graph Memory Transformer (GMT) arXiv:2604.23862v2 Announce Type: replace-cross Abstract: We investigate whether the Feed-Forward Network (FFN) sublayer in a decoder-only transformer can be replaced by an explicit learned memory graph while preserving the surrounding autoregressive architecture. The proposed Graph Memory Transformer (GMT) keeps causal self-attention intact, but replaces the usual per-token FFN transformation with a memory cell that routes token representations over a learned ban