mlsanityData to 14 Oct 2026

Learning C to x86 Translation: An Experiment in Neural Compilation

arXiv 2108.07639 ↗First posted 17 Aug 2021cs.AI cs.PLcited by 12

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Abstract (arXiv)

Deep learning has had a significant impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated. In this work, we explore neural compilation, building and evaluating Transformer models that learn how to produce x86 assembler from C code. Although preliminary results are relatively weak, we make our data, models and code publicly available to encourage further research in this area.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 17 Aug 2021.

Builds on

22 of 37 references.

References · 22

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10 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 3; 2026: 1.

12 papers.

  1. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  2. 2023Large Language Models for Compiler Optimizationcited by 52
  3. 2024Meta Large Language Model Compiler: Foundation Models of Compiler Optimizationcited by 39
  4. 2023SLaDe: A Portable Small Language Model Decompiler for Optimized Assemblycited by 28
  5. 2024Priority Sampling of Large Language Models for Compilerscited by 8
  6. 2024Learning Program Behavioral Models from Synthesized Input-Output Pairscited by 4
  7. 2024ViC: Virtual Compiler Is All You Need For Assembly Code Searchcited by 2
  8. 2024Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!cited by 1
  9. 2025QiMeng: Fully Automated Hardware and Software Design for Processor Chipcited by 0
  10. 2025QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Codecited by 0
  11. 2025Exploring the Feasibility of End-to-End Large Language Model as a Compilercited by 0
  12. 2026The New Compiler Stack: A Survey on the Synergy of LLMs and Compilerscited by 0