KV-cache quantization and linear attention are two representative approaches to tackling the storage and computational costs of Transformers.
KV-cache quantization compresses individual KV entries into discrete codes but retains all entries, whereas linear attention recurrently aggregates multiple historical KV contributions into a fixed-size continuous state but can introduce interference.
This contrast raises the question of whether per-KV compression and multi-KV aggregation can be bridged within a single mechanism for efficient attention.
We identify RAM-Net as such a bridge through soft assignments over a discrete address space.
These assignments determine recurrent updates to the continuous slot state associated with each address.
Under a restricted RAM-Net construction, we prove that soft address assignments extend hard quantized matching to a separable read-write overlap that locally approximates full-attention similarity and supports recurrent aggregation.
These connections further enable Transformer-to-RAM-Net weight migration through a new path based on a soft-quantized intermediate construction.
Across nine pretrained Transformer models from 0.3B to 7B parameters, RAM-Net recovers an average of 87.1% of the teachers' accuracy gains over random guessing across six commonsense and knowledge tasks using only a 500M-token budget per model.