# Exploring the Feasibility of End-to-End Large Language Model as a Compiler

arXiv: https://arxiv.org/abs/2511.04132 · HTML: https://mlsanity.com/paper/2511.04132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 24 of 24)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [LLM4Drive: A Survey of Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.01043/) (2023)
- [InferFix: End-to-End Program Repair with LLMs](https://mlsanity.com/paper/2303.07263/) (2023)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [LLM4Decompile: Decompiling Binary Code with Large Language Models](https://mlsanity.com/paper/2403.05286/) (2024)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models](https://mlsanity.com/paper/2310.15991/) (2023)
- [Ranking LLM-Generated Loop Invariants for Program Verification](https://mlsanity.com/paper/2310.09342/) (2023)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [End-to-End Speech Recognition Contextualization with Large Language Models](https://mlsanity.com/paper/2309.10917/) (2023)
- [LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation](https://mlsanity.com/paper/2310.04963/) (2023)
- [TLP: A Deep Learning-based Cost Model for Tensor Program Tuning](https://mlsanity.com/paper/2211.03578/) (2022)
- [Learning C to x86 Translation: An Experiment in Neural Compilation](https://mlsanity.com/paper/2108.07639/) (2021)
- [Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition](https://mlsanity.com/paper/2307.08234/) (2023)
- [ComPile: A Large IR Dataset from Production Sources](https://mlsanity.com/paper/2309.15432/) (2023)
- [Neural Architecture Search as Program Transformation Exploration](https://mlsanity.com/paper/2102.06599/) (2021)
- [LLM-Aided Compilation for Tensor Accelerators](https://mlsanity.com/paper/2408.03408/) (2024)

## Abstract (arXiv)

In recent years, end-to-end Large Language Model (LLM) technology has shown substantial advantages across various domains. As critical system software and infrastructure, compilers are responsible for transforming source code into target code. While LLMs have been leveraged to assist in compiler development and maintenance, their potential as an end-to-end compiler remains largely unexplored. This paper explores the feasibility of LLM as a Compiler (LaaC) and its future directions. We designed the CompilerEval dataset and framework specifically to evaluate the capabilities of mainstream LLMs in source code comprehension and assembly code generation. In the evaluation, we analyzed various errors, explored multiple methods to improve LLM-generated code, and evaluated cross-platform compilation capabilities. Experimental results demonstrate that LLMs exhibit basic capabilities as compilers but currently achieve low compilation success rates. By optimizing prompts, scaling up the model, and incorporating reasoning methods, the quality of assembly code generated by LLMs can be significantly enhanced. Based on these findings, we maintain an optimistic outlook for LaaC and propose practical architectural designs and future research directions. We believe that with targeted training, knowledge-rich prompts, and specialized infrastructure, LaaC has the potential to generate high-quality assembly code and drive a paradigm shift in the field of compilation.