FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
FEAT replaces quadratic attention with a multi-layer dual-axis encoding architecture and integrates an adaptive-fusion bidirectional state-space model (AFBM) with convolutional gated linear attention (Conv-GLA), enabling cross-tuple contextualization in O(N) time while supporting permutation-invariant representation le...