mlsanityData to 7 Oct 2026

RAT: RunAnyThing via Fully Automated Environment Configuration

arXiv 2604.23190 ↗First posted 25 Apr 2026cs.SE cs.AIcited by 1

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Abstract (arXiv)

Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of configuring executable environments. However, manual configuration remains a labor-intensive bottleneck, necessitating a transition toward fully automated environment configuration. Existing approaches often rely on pre-defined artifacts or are restricted to specific programming languages, limiting their applicability to diverse real-world repositories. In this paper, we first propose RAT (RunAnyThing), a modular and extensible agent framework for fully automated configuration across programming languages on arbitrary repositories. RAT adopts a multi-stage pipeline that integrates language-aware abstraction, image initialization, specialized configuration toolset, and robust sandbox. Furthermore, to enable rigorous evaluation, we propose RATBench, a benchmark reflects the comprehensive coverage of real-world repositories. Extensive experiments demonstrate that RAT achieves state-of-the-art performance, improving Environment Setup Success Rate (ESSR) by an average of 36.1% over strong baselines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 25 Apr 2026.

Builds on

26 of 35 references.

References · 26

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  5. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  6. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  7. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
  8. 2023StarCoder: may the source be with you!cited by 790
  9. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  10. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
  11. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  12. 2022Repository-Level Prompt Generation for Large Language Models of Codecited by 106
14 more
  1. 2023CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modulescited by 58
  2. 2024Repoformer: Selective Retrieval for Repository-Level Code Completioncited by 55
  3. 2024A Performance Study of LLM-Generated Code on Leetcodecited by 48
  4. 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projectscited by 45
  5. 2025Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewardscited by 41
  6. 2025Repo2Run: Automated Building Executable Environment for Code Repository at Scalecited by 39
  7. 2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
  8. 2025GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveragingcited by 25
  9. 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projectscited by 21
  10. 2026PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimizationcited by 10
  11. 2024Debugging with Open-Source Large Language Models: An Evaluationcited by 7
  12. 2026RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimizationcited by 6
  13. 2024AUTOGENICS: Automated Generation of Context-Aware Inline Comments for Code Snippets on Programming Q&A Sites Using LLMcited by 5
  14. 2025Tree of Preferences for Diversified Recommendationcited by 2

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