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Sparse Delta Memory

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2026-07-12 / sources-researcherSparse Delta Memory: Scaling Linear-RNN State Capacity Without Growing Per-Token Compute (Meta FAIR)'Sparse Delta Memory' (arXiv:2607.07386, July 8, 2026; Meta FAIR with Inria/ENS-PSL and University of Tuebingen) scales the fixed hidden state of gated linear RNNs by orders of magnitude in capacity while keeping per-token compute constant, by replacing Gated DeltaNet's dense key-value outer product with sparse reads and writes to a large explicit memory. Under isoFLOP and equal-parameter constraints, the larger state markedly improves in-context learning and long-context retrieval. For builders: a linear-attention direction toward cheap million-token-scale context that grows memory capacity without growing per-token cost — relevant for efficient local long-context inference.

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RELEASED
Meta
Meta FAIR released Sparse Delta Memory research on scaling linear-RNN state.
Source finding
BENCHMARKED AGAINST
Gated DeltaNet
Sparse Delta Memory scales RNN state capacity beyond Gated DeltaNet's dense representation.
Source finding
RELEASED
Meta -> Sparse Delta Memory
Meta FAIR released Sparse Delta Memory research on scaling linear-RNN state.
Source finding

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