SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks

In three linesSinkRec introduces a hybrid memory-transition architecture for long-sequence recommendation using linear attention and conditional memory. It addresses semantic state sink—where repetitive patterns dominate recurrent state—by externalizing recurring behaviors via residual vector quantization and introducing Temporal-Aware State-Relation Differential Gated DeltaNet (TDGD) to purify state reads and writes.

Summary generated by Claude — human-verified