Exact Linear Attention
The paper titled 'Exact Linear Attention' introduces a new mechanism for Transformer attention that achieves linear computational complexity. It addresses issues found in previous linear attention methods by imposing kernel constraints to ensure better performance. The author also presents several engineering innovations to enhance the attention mechanism's interpretability and effectiveness.
- ▪Exact Linear Attention (ELA) achieves linear computational complexity for Transformer attention without approximation error.
- ▪The paper proposes several kernel functions to address gradient explosion and token attention dilution.
- ▪Innovations include a Hyper Link structure, a Memory Lobe module, and a routing score based bias mechanism.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18848 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | VX_cbVFhUor_ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.18848 (cs) [Submitted on 13 May 2026] Title:Exact Linear Attention Authors:Weinuo Ou View a PDF of the paper titled Exact Linear Attention, by Weinuo Ou View PDF HTML (experimental) Abstract:This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by leveraging the exact decomposition property of kernel functions, without any approximation error. It identifies and addresses gradient explosion and token attention dilution in prior linear attention methods by imposing kernel constraints that ensure non-negativity, discriminability, and geometric interpretability.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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