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arxiv: 2604.23190 · v1 · submitted 2026-04-25 · 💻 cs.SE · cs.AI

RAT: RunAnyThing via Fully Automated Environment Configuration

Renhong Huang , Dongdong Hua , Yifei Sun , Sitao Ding , Hanyang Yuan , Daixin Wang , Yang Yang This is my paper

Pith reviewed 2026-05-08 08:01 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords automated environment configurationsoftware repositoriescode agentslanguage-agnosticenvironment setupRATBenchautonomous software engineeringsandboxed configuration
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The pith

RAT enables fully automated environment configuration for arbitrary software repositories using a language-agnostic multi-stage pipeline.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Repository-level software engineering tasks for autonomous code agents often stall at the manual step of configuring executable environments across diverse codebases. RAT proposes a framework that automates this process without relying on pre-defined artifacts or restricting itself to particular programming languages. The approach combines semantic initialization to understand repository structure, a planning mechanism to sequence configuration steps, specialized tools for execution, and a sandbox to test and isolate the setup safely. Evaluation on a new benchmark called RATBench, built to capture the variety of real-world repositories, shows consistent gains over prior methods. If correct, this removes a major barrier that currently limits agents to narrow sets of projects.

Core claim

The paper claims that the RAT framework, through its multi-stage pipeline of semantic initialization, planning, specialized toolset, and robust sandbox, achieves state-of-the-art performance on automated environment configuration for arbitrary repositories, raising the Environment Setup Success Rate by an average of 29.6 percent compared with strong baselines when tested on RATBench, a benchmark that mirrors the distribution and heterogeneity of real-world codebases.

What carries the argument

The RAT multi-stage pipeline that integrates semantic initialization for repository understanding, a planning mechanism for step sequencing, specialized tools for configuration actions, and a sandbox for safe execution and validation.

Load-bearing premise

The multi-stage pipeline can reliably handle the varied structures, dependencies, and heterogeneity of arbitrary real-world repositories without manual intervention or language-specific restrictions.

What would settle it

A large-scale test on repositories with unusual or non-standard dependency setups where RAT's Environment Setup Success Rate falls to or below the level of existing baselines.

Figures

Figures reproduced from arXiv: 2604.23190 by Daixin Wang, Dongdong Hua, Hanyang Yuan, Renhong Huang, Sitao Ding, Yang Yang, Yifei Sun.

Figure 1
Figure 1. Figure 1: The architecture of RAT (RunAnyThing). The framework consists of several primary modules: (1) Language-Agnostic Abstraction, which identifies project languages and encapsulates domain-specific protocols into a unified interface; (2) ImageRetriever, which performs semantic analysis of the repository to select optimal base images; (3) Agent Planning, featuring both a fixed Standard Plan Mode and an adaptive … view at source ↗
Figure 2
Figure 2. Figure 2: Performance across different execution steps as budget. As the number of steps increases, the ESSR improves significantly, as well as at the cost of average token consumption and average latency. 6.2. Environment configuration. The research focus for repository-level tasks has shifted from isolated code generation to the challenges of environ￾ment configuration. While early benchmarks like SWE￾Bench (Jimen… view at source ↗
Figure 3
Figure 3. Figure 3: Repository size distribution across languages in RAT￾Bench view at source ↗
Figure 4
Figure 4. Figure 4: Repository popularity (GitHub stars) distribution by language in RATBench view at source ↗
Figure 5
Figure 5. Figure 5: Distributions of tokens, latency, and pass rates across repositories. 0.0 0.2 0.4 0.6 0.8 1.0 Tokens (×10 6 ) 0 1000 2000 3000 4000 L a t e n c y ( s ) Pearson r=0.618 view at source ↗
Figure 6
Figure 6. Figure 6: Correlation between token consumption and model latency. Case Study on Trajectories. In this section, we take stlehmann/Flask-MQTT as an example repository to illustrate the different trajectory between RAT and Repo2run as shown in view at source ↗
Figure 7
Figure 7. Figure 7: Trajectory comparison between RAT and Repo2Run on repository stlehmann/Flask-MQTT. and change-python-version, are invoked less often, since most issues can be resolved using the LLM’s intrinsic reasoning and debugging capabilities. Overall, the effective utilization of these tools demonstrates the soundness and rationality of our tool design. 0.00 0.05 0.10 0.15 0.20 0.25 retrieve-issue search-web detect-e… view at source ↗
Figure 8
Figure 8. Figure 8: Tool calls distribution of RAT across Python repositories in RATBench. Failure Analysis view at source ↗
Figure 9
Figure 9. Figure 9: Breakdown of pytest error types for Python repositories where RAT fails to solve. Action Call Illustration. The Action Call Example for repository abrignoni/aleapp as shown in view at source ↗
read the original abstract

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 real-world repositories. In this paper, we first propose RAT (RunAnyThing), a language-agnostic framework for automated environment configuration on arbitrary repositories. RAT features a multi-stage pipeline that integrates semantic initialization, a planning mechanism, specialized toolset, and a robust sandbox for configuration. Furthermore, to enable rigorous evaluation, we propose RATBench, a benchmark that reflects the the distribution and heterogeneity of real-world repositories. Extensive experiments demonstrate that RAT achieves state-of-the-art performance, improving the Environment Setup Success Rate (ESSR) by an average of 29.6% over strong baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes RAT (RunAnyThing), a language-agnostic multi-stage pipeline for fully automated environment configuration of arbitrary repositories. The pipeline integrates semantic initialization, planning, a specialized toolset, and a sandbox. To support evaluation, the authors introduce RATBench, a benchmark intended to capture the distribution and heterogeneity of real-world repositories. Extensive experiments are claimed to demonstrate state-of-the-art performance, with RAT improving Environment Setup Success Rate (ESSR) by an average of 29.6% over strong baselines.

Significance. If the empirical claims hold under rigorous scrutiny, the work would meaningfully advance autonomous code agents by addressing the manual environment-configuration bottleneck that currently limits repository-level tasks. The introduction of RATBench as a benchmark reflecting real-world heterogeneity is a constructive contribution that could enable more standardized future evaluations. The language-agnostic design is also a strength relative to prior language-restricted approaches.

major comments (3)
  1. [Experiments] Experiments section: The headline 29.6% average ESSR improvement is reported without error bars, standard deviations, number of runs, or statistical significance tests. This makes it impossible to determine whether the gain is robust or could be explained by variance in the chosen repositories.
  2. [§3] §3 (RATBench construction): The repository sampling procedure, exact definition of the ESSR success criterion ('environment fully executable for downstream tasks'), and controls for curation bias are not described in sufficient detail. Without these, it cannot be verified that RATBench faithfully represents the long tail of dependency, build-system, and language heterogeneity.
  3. [Experiments] Baseline re-implementation details (Experiments section): The paper does not specify how the 'strong baselines' were re-implemented, including whether they received equivalent tool access, sandbox tolerances, or planning capabilities. This information is load-bearing for the SOTA claim.
minor comments (2)
  1. [Abstract] Abstract: 'reflects the the distribution' contains a duplicated word.
  2. [Abstract] The first use of the ESSR acronym should be accompanied by its expansion for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for providing detailed and constructive feedback that will help improve the clarity and rigor of our paper. Below, we respond to each major comment and indicate the corresponding revisions.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: The headline 29.6% average ESSR improvement is reported without error bars, standard deviations, number of runs, or statistical significance tests. This makes it impossible to determine whether the gain is robust or could be explained by variance in the chosen repositories.

    Authors: We agree that the current reporting of the 29.6% average ESSR improvement lacks the statistical details necessary to assess robustness. In the revised manuscript, we will expand the Experiments section to include error bars (standard deviation across runs), explicitly state the number of independent runs performed per repository, and report the results of statistical significance tests (such as paired t-tests or Wilcoxon signed-rank tests) comparing RAT against each baseline. These additions will allow readers to evaluate whether the observed gains are statistically reliable rather than attributable to variance. revision: yes

  2. Referee: [§3] §3 (RATBench construction): The repository sampling procedure, exact definition of the ESSR success criterion ('environment fully executable for downstream tasks'), and controls for curation bias are not described in sufficient detail. Without these, it cannot be verified that RATBench faithfully represents the long tail of dependency, build-system, and language heterogeneity.

    Authors: We acknowledge that Section 3 would benefit from greater specificity to substantiate RATBench's representativeness. We will revise this section to detail: (1) the repository sampling procedure, including data sources, filtering criteria (e.g., by language, build system, dependency complexity, and activity level), and stratification to capture heterogeneity; (2) the precise operational definition of the ESSR success criterion, specifying the exact conditions for an environment to be deemed 'fully executable for downstream tasks' (e.g., successful dependency resolution, build completion, and execution of representative scripts or tests); and (3) controls for curation bias, such as quantitative diversity metrics across languages and build systems. These clarifications will better demonstrate alignment with real-world repository distributions. revision: yes

  3. Referee: [Experiments] Baseline re-implementation details (Experiments section): The paper does not specify how the 'strong baselines' were re-implemented, including whether they received equivalent tool access, sandbox tolerances, or planning capabilities. This information is load-bearing for the SOTA claim.

    Authors: We recognize that transparent baseline re-implementation details are critical to supporting the SOTA claim. In the revised Experiments section, we will provide a dedicated subsection describing the re-implementation of each baseline, including the exact tool access granted, sandbox configurations and tolerances applied, and any planning or reasoning modules provided. We will also note and justify any necessary differences arising from RAT's language-agnostic design. This will enable a clear assessment of fairness in the comparisons. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on external benchmark comparisons

full rationale

The paper introduces RAT as a multi-stage pipeline for automated environment configuration and RATBench as a new benchmark reflecting real-world repository distributions, then reports an empirical 29.6% ESSR improvement over baselines. No equations, parameter fits, or derivations appear in the provided text; the central result is an experimental outcome from running the system on the benchmark rather than any quantity defined in terms of itself or reduced via self-citation. The pipeline components (semantic initialization, planning, tools, sandbox) are presented as design choices evaluated externally, with no load-bearing uniqueness theorems or ansatzes imported from prior author work. This is a standard empirical software-engineering paper whose validity hinges on benchmark representativeness and baseline fairness, not on internal definitional closure.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the unverified effectiveness of the multi-stage pipeline for arbitrary repositories and the representativeness of RATBench; these are introduced without independent evidence in the abstract.

axioms (1)
  • domain assumption Semantic analysis of repository contents can determine required environment configurations across languages.
    Invoked as the basis for the semantic initialization stage.
invented entities (1)
  • RAT multi-stage pipeline no independent evidence
    purpose: Automated environment configuration for arbitrary repositories
    New framework proposed by the paper with no external falsifiable evidence provided in the abstract.

pith-pipeline@v0.9.0 · 5469 in / 1278 out tokens · 61474 ms · 2026-05-08T08:01:41.896810+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

  1. [1]

    Use an appropriate base image for the programming language

    work page

  2. [2]

    Set up the correct working directory

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  3. [3]

    Copy all necessary files

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  4. [4]

    Install all dependencies

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  5. [5]

    Set appropriate environment variables if needed Output ONLY the Dockerfile content without any explanation or markdown code blocks. Based on the following repository information, generate a production-ready Dockerfile: Repository: {repo_name} {repo_context} Generate a Dockerfile that: - Installs all required dependencies - Sets up all variables and config...

    work page

  6. [6]

    Analyze Dependencies:

    work page

  7. [7]

    Install Dependencies:

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  8. [8]

    Verify Installation:

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  9. [9]

    Fix Environment Issues:

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  10. [10]

    *test*.py

    Validate Setup: ... ## Code Modification Policy ... ## Important Notes - Focus on installing dependencies and configuring the environment - ... problem_statement_template: |- This is a {{language}} repository that requires environment setup and configuration. ## Objective Configure the repository to be ready for running tests successfully. ## Detailed Ste...

    work page 2000

  11. [11]

    **Identify the primary language ** (...)

    work page

  12. [12]

    **Analyze version requirements **:

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  13. [13]

    slim, alpine), use default_variant by default

    **Select the best image **: - Prefer selecting language/version/variant from ALLOWED_VERSIONS.json - If the project requires a specific version, pick the closest allowed version - If version is unclear, use default_version - For variant (e.g. slim, alpine), use default_variant by default

    work page

  14. [14]

    language

    **Extract dependency info ** (optional): key frameworks and dependencies ## Output format (raw JSON only; no Markdown) {{ "language": ..., "version": ..., "variant": ..., "base_image": ..., "full_image": ..., "reason": ..., "confidence": ..., "frameworks": ..., "dependencies": ... }} ## Important rules

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  15. [15]

    **Version constraint **:

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    **Variant constraint **:

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  17. [17]

    }} 23 RAT: RunAnyThing via Fully Automated Environment Configuration Prompt for Standard Plan Mode

    ## Example output {{ ... }} 23 RAT: RunAnyThing via Fully Automated Environment Configuration Prompt for Standard Plan Mode. Standard Plan Mode You are an expert in environment setup. You may refer to files and structures in the repository such as requirements.txt, setup.py, etc., and use dependency inference tools like pipreqs to install third-party libr...

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  18. [18]

    Explore the repository to understand its full structure and the image environment

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  19. [19]

    Find the program entry point; create a usable test case; check whether tests pass without extra setup

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  20. [20]

    Read key documentation, including

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  21. [21]

    Inspect repository directories and read environment-related files

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  22. [22]

    Collect dependency lists: find dependency files in the repo root

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  23. [23]

    Install dependencies based on collected files

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  24. [24]

    CLI tool instructions: All operations run inside Docker container {image_name} Think about what to do next, then wrap commands with

    Check whether tests pass; if so, call stop. CLI tool instructions: All operations run inside Docker container {image_name} Think about what to do next, then wrap commands with ... Note: Do not make large changes to /repo; only necessary adjustments. Keep commands on a single line when possible using &&. Avoid multi-line commands, backslash continuations, ...

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  25. [25]

    First check whether ‘/repo/plan.md‘ exists

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  26. [26]

    configure {image_name} environment so tests pass

    If it does not exist, you should: - Explore repository structure and understand the project - Create an initial ‘plan.md‘ based on your analysis (make steps concrete; avoid too many generic steps) - Use the ‘edit-file‘ tool to create the file; use the template below: ‘‘‘markdown # Task Plan: Environment Configuration for {image_name} ## Goal [Goal of envi...

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  27. [27]

    ### Phase 2: Execute the plan

    If the file already exists, read and understand the existing plan. ### Phase 2: Execute the plan

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  28. [28]

    Read the current plan and understand the current phase and goal

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  29. [29]

    25 RAT: RunAnyThing via Fully Automated Environment Configuration

    Find the current phase (an unchecked ‘[ ]‘ phase). 25 RAT: RunAnyThing via Fully Automated Environment Configuration

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  30. [30]

    Execute tasks in the current phase: - If the phase status is not marked, update it to ‘in_progress‘ using ‘edit-file‘ - Execute tasks (explore/install/configure/test) - After finishing the phase, update ‘[ ]‘ to ‘[x]‘ and set status to ‘complete‘

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  31. [31]

    ## Response format requirements

    Continue to the next phase until all phases are complete ## Important rules ... ## Response format requirements ... **Example:** ... ## Initial action guide ... Available tools: {tools_list} Prompt for Calling Different Actions. Action Calling You are an expert proficient in testing. The current environment has been built using the repository’s Dockerfile...

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  32. [32]

    Verify that the environment is working correctly

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  33. [33]

    Run tests and ensure they pass Workflow:

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  34. [34]

    Quick environment verification: Check if key commands and dependencies are available

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  35. [35]

    Use the construct-test tool to create test cases

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  36. [36]

    Run tests (run-pytest-collect and run-pytest)

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  37. [37]

    for example: ### Thought:

    CLI Tool Usage Instructions: All operations are performed inside Docker container {image_name} ... for example: ### Thought: ... ### Action: {BASH_FENCE[0]} ... {BASH_FENCE[1]} 26 RAT: RunAnyThing via Fully Automated Environment Configuration Available tools (callable but not terminal built-in commands): {tools_list} Important Notes:

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  38. [38]

    Environment has been built via repository Dockerfile, most dependencies should already be installed

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  39. [39]

    Special Note: ... 27

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