mlsanityData to 7 Oct 2026

Agentic Harness for Real-World Compilers

arXiv 2603.20075 ↗First posted 20 Mar 2026cs.SE cs.AIcited by 3

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable automated bug repair, compiler bugs pose unique challenges due to their complexity, deep cross-domain expertise requirements, and sparse, non-descriptive bug reports, necessitating compiler-specific harnesses. To bridge the gap, we introduce llvm-harness, the first harness designed to assist LLM agents in understanding and fixing compiler bugs. Our current focus is on the middle end of LLVM, one of the most widely used compiler infrastructures. Central to llvm-harness are agent-friendly LLVM tools, a benchmark llvm-bench of 334 reproducible LLVM middle-end bugs, and a tailored mini agent llvm-autofix-mini for fixing LLVM middle-end bugs automatically. We evaluate five frontier models and find that they exhibit a performance decline when tackling compiler bugs with the state-of-the-art agent. With llvm-harness' enhancement, their performance improves by 62%. Our specialized mini agent llvm-autofix-mini further outperforms the llvm-harness-enhanced state-of-the-art by 22%. This emphasizes the necessity for specialized harnesses like ours to assist LLMs in compiler engineering tasks. Despite promising results, our expert review also reveals several open challenges that remain when applying LLMs for compiler engineering tasks. GitHub: https://github.com/dtcxzyw/llvm-harness

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 18 of its 47 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

18 of 47 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System7 May
  2. 2025LocAgent: Graph-Guided LLM Agents for Code Localization12 Mar
  3. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2025KernelBench: Can LLMs Write Efficient GPU Kernels?cited by 206
  4. 2023Fuzz4All: Universal Fuzzing with Large Language Modelscited by 88
  5. 2021CompilerGym: Robust, Performant Compiler Optimization Environments for AI Researchcited by 41
  6. 2018A Survey on Compiler Autotuning using Machine Learningcited by 40
  7. 2021MLGO: a Machine Learning Guided Compiler Optimizations Frameworkcited by 40
  8. 2023WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Modelscited by 37
  9. 2024LLM-Vectorizer: LLM-based Verified Loop Vectorizercited by 25
  10. 2023LLM4VV: Developing LLM-Driven Testsuite for Compiler Validationcited by 15
  11. 2024KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolutioncited by 12
  12. 2022RL4ReAl: Reinforcement Learning for Register Allocationcited by 4
3 more
  1. 2025LPO: Discovering Missed Peephole Optimizations with Large Language Modelscited by 4
  2. 2025Interleaving Large Language Models for Compiler Testingcited by 3
  3. 2025Rethinking Kernel Program Repair: Benchmarking and Enhancing LLMs with RGymcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025HarnessAgent: Scaling Automatic Fuzzing Harness Construction with Tool-Augmented LLM Pipelines
  2. 2026Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports
  3. 2026SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
  4. 2024An Empirical Study on LLM-based Agents for Automated Bug Fixing
  5. 2026When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
  6. 2026AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
  7. 2026HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks
  8. 2026AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces