Kalman Delta Networks: Uncertainty-aware Associative Memory
Linear attention enables efficient long-context inference by compressing token history into a fixed-size recurrent memory. This compression makes each update a trade-off between incorporating new information and preserving useful associations. Models such as DeltaNet, Gated DeltaNet, and KDA predict write strength from the current token representation, without explicitly tracking uncertainty in the stored memory. Yet this uncertainty matters: a new observation should have greater influence when the existing association is uncertain and less when it is already well supported. We introduce Kalman Delta Networks (KDNs), a family of linear-attention models that explicitly track memory uncerta...
| Authors | Ngoc Bui, Tinglin Huang, Rex Ying |
|---|---|
| Primary category | cs.LG |
| Published | 7 September 2026 |
| Last updated at source | 29 September 2026 |
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