What is recorded here?
Extended gated delta updates with finer-grained memory gating and a specialized chunkwise algorithm, integrating the module with latent attention in a hybrid language model.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits Gated DeltaNet, delta-rule fast weights and MLA. Reported cache/performance gains are tested regimes, not universal replacement guarantees or exact dense attention. Individual appendix contributors are not automatically front-page authors.
Read the original work
- Kimi Linear: An Expressive, Efficient Attention Architecture
- Kimi Team: Kimi Delta Attention and hardware-efficient linear mixing — official method source
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities. The recorded author list is incomplete.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Kimi Team: Kimi Delta Attention and hardware-efficient linear mixing
Extended gated delta updates with finer-grained memory gating and a specialized chunkwise algorithm, integrating the module with latent attention in a hybrid language model.
Scientific scope
Inherits Gated DeltaNet, delta-rule fast weights and MLA. Reported cache/performance gains are tested regimes, not universal replacement guarantees or exact dense attention. Individual appendix contributors are not automatically front-page authors.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.