---
title: 'Attention Is All You Need'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/1706-03762-attention-is-all-you-need.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, Attention Is All You Need (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: foundations'
tags: arxiv, transformers, attention, foundations
arxiv_id: 1706.03762
alias: 1706.03762-attention-is-all-you-need
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Attention Is All You Need'
    publisher: arXiv
    authors: 'Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin'
    url: https://arxiv.org/abs/1706.03762
    version_read: 'v7 (2 Aug 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Attention Is All You Need

## Summary
[Attention Is All You Need](https://arxiv.org/abs/1706.03762) (2017) is the paper that introduced the Transformer. It showed that a sequence model built from self-attention and feed-forward layers, with positional encodings and no recurrence or convolution in the main stack, could do machine translation at a high level and train efficiently on then-current hardware.

What it introduced: the encoder-decoder Transformer, multi-head self-attention, and the training recipe that made that architecture usable.

What it led to: almost the entire modern language, vision, and multimodal stack. Downstream papers in this batch include the pointers for BERT, GPT-3, and FlashAttention. Live library notes that sit on top of the same architecture include [hf-transformers-attention-backends](https://iinventedlight.com/library/notes/hf-transformers-attention-backends/).

If you only read one foundations paper in this set, this is the one. Where to read it: https://arxiv.org/abs/1706.03762 (v7, last revised 2 Aug 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).