NLP Lineage

6 May 2024 · Representation and retrieval

SWE-agent

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

J. Yang, C. Jimenez, A. Wettig, et al. (7 authors)

Date
6 May 2024
What the date marks
arXiv submission
Kind
Research paper
Organisation
Princeton
Access
MIT
Other dates
NeurIPS 2024
Threads
In the guided tour
Chapter 13. Models that act
Learning phases
7. Agents

Notable: no later milestone in the data is documented as descending from it. How levels are decided

Read the primary source (arxiv.org)

Full summary in progress. Until then, this page shows the milestone's record from the research data.

What it changed

Before it

Agents were given raw shell access and expected to cope.

The problem

Interfaces built for humans are poor interfaces for models, which have no screen and limited context.

What it introduced

Design the interface for the agent: scoped file viewing, guarded edits with linting, and concise feedback.

Why it matters

It showed a large share of agent performance comes from interface design rather than model capability.

What modern models inherit

Tool design as a first-class part of agent engineering.

What came next

The agent-computer interface idea runs through every serious coding agent since.

Before and after

The milestones it descends from, and the milestones that descend from it. Work developed alongside it is not counted. Weak links are left out unless you include them.

Before: where it came from

SWE-agent has 41 documented ancestors across 9 chapters, reaching back to Recurrent networks in 1990. Showing its direct parents and the 17 turning points among them.

SWE-agent has 41 documented ancestors across 9 chapters, reaching back to Recurrent networks in 1990. Showing all of them.

SWE-agent has 43 recorded ancestors, including indirect ones, across 9 chapters, reaching back to Recurrent networks in 1990. Showing its direct parents and the 17 turning points among them.

SWE-agent has 43 recorded ancestors, including indirect ones, across 9 chapters, reaching back to Recurrent networks in 1990. Showing all of them.

  1. 2024SWE-agentThis milestone.
  2. 2023SWE-benchSWE-agent built directly on thisChapter 13. Models that act
  3. 2023GPT-4 Turning pointSWE-bench was influenced by thisChapter 7. The assistant goes public
  4. 2022GPT-3.5 Turning pointGPT-4 succeeded thisChapter 7. The assistant goes public
  5. 2022ReActSWE-agent was influenced by thisChapter 13. Models that act
  6. 2022InstructGPT Turning pointGPT-4 built directly on thisChapter 6. Teaching models what people want
  7. 2022Chain of thought Turning pointReAct built directly on thisChapter 12. Thinking longer
  8. 2021WebGPTReAct was influenced by thisChapter 13. Models that act
  9. 2021FLANInstructGPT was influenced by thisChapter 6. Teaching models what people want
  10. 2021Codex / HumanEvalSWE-bench built directly on thisChapter 13. Models that act
  11. 2020RLHF for summarisation Turning pointWebGPT built directly on thisChapter 6. Teaching models what people want
  12. 2020GPT-3 Turning pointChain of thought was influenced by this
    Codex / HumanEval built directly on this
    WebGPT built directly on this    Chapter 5. Scale becomes a science
  13. 2020Scaling laws Turning pointGPT-4 built directly on thisChapter 5. Scale becomes a science
  14. 2019T5 Turning pointFLAN was influenced by thisChapter 4. Pretrain once, use everywhere
  15. 2019C4 Turning pointT5 was influenced by thisChapter 4. Pretrain once, use everywhere
  16. 2019RLHF for LMsRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  17. 2019Sparse Transformer Turning pointGPT-3 built directly on thisChapter 4. Pretrain once, use everywhere
  18. 2019GPT-2 Turning pointScaling laws was influenced by this
    GPT-3 built directly on this    Chapter 4. Pretrain once, use everywhere
  19. 2019WebTextGPT-2 was influenced by thisChapter 4. Pretrain once, use everywhere
  20. 2018BERT Turning pointT5 was influenced by thisChapter 4. Pretrain once, use everywhere
  21. 2018SentencePieceT5 built directly on thisChapter 4. Pretrain once, use everywhere
  22. 2018GPT-1GPT-2 built directly on thisChapter 4. Pretrain once, use everywhere
  23. 2018ELMoBERT was influenced by thisChapter 4. Pretrain once, use everywhere
  24. 2018ULMFiTBERT was influenced by thisChapter 4. Pretrain once, use everywhere
  25. 2017CoVeELMo was influenced by thisChapter 3. Attention, then only attention
  26. 2017Transformer Turning pointScaling laws built directly on thisChapter 3. Attention, then only attention
  27. 2017RLHF originRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  28. 2016Layer normTransformer built directly on this
    GPT-2 built directly on this    Chapter 3. Attention, then only attention
  29. 2015Residual connectionsTransformer was influenced by thisChapter 3. Attention, then only attention
  30. 2015LM pretraining Turning pointGPT-1 was influenced by thisChapter 2. Words become vectors
  31. 2015BPE tokenizationGPT-2 built directly on thisChapter 2. Words become vectors
  32. 2015Luong attentionTransformer was influenced by thisChapter 3. Attention, then only attention
  33. 2014AdamTransformer was influenced by thisChapter 2. Words become vectors
  34. 2014GloVeELMo built directly on thisChapter 2. Words become vectors
  35. 2014Seq2seq Turning pointTransformer built directly on thisChapter 2. Words become vectors
  36. 2014Attention Turning pointTransformer built directly on thisChapter 3. Attention, then only attention
  37. 2014GRU / encoder-decoderAttention built directly on thisChapter 2. Words become vectors
  38. 2013word2vecELMo was influenced by thisChapter 2. Words become vectors
  39. 2011NLP from scratchELMo was influenced by thisChapter 1. Language as probability
  40. 2003Neural LMNLP from scratch was influenced by this
    word2vec was influenced by this    Chapter 1. Language as probability
  41. 1997LSTM Turning pointSeq2seq built directly on thisChapter 1. Language as probability
  42. 1990Recurrent networksLSTM was influenced by thisChapter 1. Language as probability
  1. 2024SWE-agentThis milestone.
  2. 2023SWE-benchSWE-agent built directly on thisChapter 13. Models that act
  3. 2023GPT-4 Turning pointSWE-bench was influenced by thisChapter 7. The assistant goes public
  4. 2022GPT-3.5 Turning pointGPT-4 succeeded thisChapter 7. The assistant goes public
  5. 2022ReActSWE-agent was influenced by thisChapter 13. Models that act
  6. 2022InstructGPT Turning pointGPT-4 built directly on thisChapter 6. Teaching models what people want
  7. 2022Chain of thought Turning pointReAct built directly on thisChapter 12. Thinking longer
  8. 2021WebGPTReAct was influenced by thisChapter 13. Models that act
  9. 2021FLANInstructGPT was influenced by thisChapter 6. Teaching models what people want
  10. 2021Codex / HumanEvalSWE-bench built directly on thisChapter 13. Models that act
  11. 2020RLHF for summarisation Turning pointWebGPT built directly on thisChapter 6. Teaching models what people want
  12. 2020GPT-3 Turning pointChain of thought was influenced by this
    Codex / HumanEval built directly on this
    WebGPT built directly on this    Chapter 5. Scale becomes a science
  13. 2020Scaling laws Turning pointGPT-4 built directly on thisChapter 5. Scale becomes a science
  14. 2019T5 Turning pointFLAN was influenced by thisChapter 4. Pretrain once, use everywhere
  15. 2019C4 Turning pointT5 was influenced by thisChapter 4. Pretrain once, use everywhere
  16. 2019RLHF for LMsRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  17. 2019Sparse Transformer Turning pointGPT-3 built directly on thisChapter 4. Pretrain once, use everywhere
  18. 2019GPT-2 Turning pointScaling laws was influenced by this
    GPT-3 built directly on this    Chapter 4. Pretrain once, use everywhere
  19. 2019WebTextGPT-2 was influenced by thisChapter 4. Pretrain once, use everywhere
  20. 2019Transformer-XLSparse Transformer was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  21. 2018BERT Turning pointT5 was influenced by thisChapter 4. Pretrain once, use everywhere
  22. 2018SentencePieceT5 built directly on thisChapter 4. Pretrain once, use everywhere
  23. 2018GPT-1GPT-2 built directly on thisChapter 4. Pretrain once, use everywhere
  24. 2018ELMoGPT-1 was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  25. 2018ULMFiTGPT-1 was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  26. 2017CoVeELMo was influenced by thisChapter 3. Attention, then only attention
  27. 2017Transformer Turning pointScaling laws built directly on thisChapter 3. Attention, then only attention
  28. 2017RLHF originRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  29. 2017ConvS2STransformer was influenced by this (indirect influence)Chapter 3. Attention, then only attention
  30. 2016Layer normTransformer built directly on this
    GPT-2 built directly on this    Chapter 3. Attention, then only attention
  31. 2015Residual connectionsTransformer was influenced by thisChapter 3. Attention, then only attention
  32. 2015LM pretraining Turning pointGPT-1 was influenced by thisChapter 2. Words become vectors
  33. 2015BPE tokenizationGPT-2 built directly on thisChapter 2. Words become vectors
  34. 2015Luong attentionTransformer was influenced by thisChapter 3. Attention, then only attention
  35. 2014AdamTransformer was influenced by thisChapter 2. Words become vectors
  36. 2014GloVeELMo built directly on thisChapter 2. Words become vectors
  37. 2014Seq2seq Turning pointTransformer built directly on thisChapter 2. Words become vectors
  38. 2014Attention Turning pointTransformer built directly on thisChapter 3. Attention, then only attention
  39. 2014GRU / encoder-decoderAttention built directly on thisChapter 2. Words become vectors
  40. 2013word2vecELMo was influenced by thisChapter 2. Words become vectors
  41. 2011NLP from scratchELMo was influenced by thisChapter 1. Language as probability
  42. 2003Neural LMNLP from scratch was influenced by this
    word2vec was influenced by this    Chapter 1. Language as probability
  43. 1997LSTM Turning pointSeq2seq built directly on thisChapter 1. Language as probability
  44. 1990Recurrent networksLSTM was influenced by thisChapter 1. Language as probability

After: what it led to

No documented descendants are recorded for SWE-agent without weak links.

No documented descendants are recorded for SWE-agent without weak links.

SWE-agent has 1 recorded descendants, including indirect ones, across 1 chapter, reaching forward to Claude Code in 2025. Showing all of them.

SWE-agent has 1 recorded descendants, including indirect ones, across 1 chapter, reaching forward to Claude Code in 2025. Showing all of them.

  1. 2024SWE-agentThis milestone.
  2. 2025Claude Codewas influenced by SWE-agent (indirect influence)Chapter 13. Models that act

Direct links and their sources

Where it came from

Built directly on

Influenced by

Developed alongside

What it led to

Influenced