BCMT: Blockwise Causal Memory Transformer
Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length. We introduce BCMT (Blockwise Causal Memory Transformer), an architecture for long-context language modeling that decouples local token interactions from global context propagation. Dense causal self-attention is applied independently within local blocks, while each block produces an adaptive summary aggregated through an exponential causal memory. This memory is subsequently injected back into the token representations, enabling efficient propagation of long-range contextual information without relying on explicit global...
| Authors | Rachid Arezki |
|---|---|
| Primary category | cs.CL |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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