Before Q, K, and V: Reconstructing the Transformer
Deep Learning Before Q, K, and V: Reconstructing the Transformer Many Transformer explainers start with the finished architecture. Sankar Srinivasan Aug 8, 2026 30 min read Share Image by Antonio Janeski via Unsplash Do we really need keys, queries, values, and dot product attention? “You need keys and queries for tokens to talk to each other,” says the popular Internet analogy.
- ▪Deep Learning Before Q, K, and V: Reconstructing the Transformer Many Transformer explainers start with the finished architecture.
- ▪Sankar Srinivasan Aug 8, 2026 30 min read Share Image by Antonio Janeski via Unsplash Do we really need keys, queries, values, and dot product attention?
- ▪“You need keys and queries for tokens to talk to each other,” says the popular Internet analogy.
Towards Data Science files mainly under ai. We currently carry 126 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/before-q-k-and-v-reconstructing-the-transformer/ |
| Publication time | Sat, 08 Aug 2026 15:00:00 +0000 |
| Retrieval time | 2026-08-08T15:00:42.147Z |
| Last seen | 2026-08-08T15:00:42.147Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
Deep Learning Before Q, K, and V: Reconstructing the Transformer Many Transformer explainers start with the finished architecture. We ask why it looks the way it does. Sankar Srinivasan Aug 8, 2026 30 min read Share Image by Antonio Janeski via Unsplash Do we really need keys, queries, values, and dot product attention? “You need keys and queries for tokens to talk to each other,” says the popular Internet analogy. But why? There’s a lot of great analogies for how they work, but a lot less material about why we truly need them. Are there any alternatives or are these abstract concepts inevitable? This might seem like a silly question given the utter success of the Transformer architecture in 2026.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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