# Understanding Agent-Based Patching of Compiler Missed Optimizations

arXiv: https://arxiv.org/abs/2607.02370 · HTML: https://mlsanity.com/paper/2607.02370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [Getafix: Learning to Fix Bugs Automatically](https://mlsanity.com/paper/1902.06111/) (2019)
- [WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models](https://mlsanity.com/paper/2310.15991/) (2023)
- [PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification](https://mlsanity.com/paper/2502.02747/) (2025)
- [Identifying Patch Correctness in Test-Based Program Repair](https://mlsanity.com/paper/1706.09120/) (2017)
- [Souper: A Synthesizing Superoptimizer](https://mlsanity.com/paper/1711.04422/) (2017)
- [LPO: Discovering Missed Peephole Optimizations with Large Language Models](https://mlsanity.com/paper/2508.16125/) (2025)
- [Agentic Harness for Real-World Compilers](https://mlsanity.com/paper/2603.20075/) (2026)
- [Leveraging Large Language Models for Generalizing Peephole Optimizations](https://mlsanity.com/paper/2603.18477/) (2026)

## Abstract (arXiv)

Compiler missed optimizations refer to cases in which compilers failed to optimize certain code. It takes many compiler developers' efforts to implement or patch such missed optimizations. In this paper, we present a systematic study of how well agents patch compiler missed optimizations. We identify a significant challenge that patching a missed optimization requires more than just fixing the reported case, and instead requires generalizing to similar cases. We construct a benchmark of real-world LLVM missed optimization issues and compare agent-generated patches with patches from developers in terms of optimization scope. Our results show that coding agents often optimize the given examples, but many generated patches either cover only part of the developer-intended scope or partially overlap with it; in some cases, they further generalize beyond the reference patch. We further introduce historical-knowledge augmentation techniques that leverage prior LLVM optimization pull requests through retrieval and distillation, showing that they improve developer-aligned generalization and yield practical benefits when applied to real-world IR.