arxiv: 2604.02382 · v1 · submitted 2026-04-01 · 💻 cs.SE · cs.AI

Recognition: 1 theorem link

· Lean Theorem

Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis

Zhenning Yang , Kaden Gruizenga , Tongyuan Miao , Patrick Tser Jern Kon , Hui Guan , Ang Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-13 21:48 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords Infrastructure-as-Codeambiguity resolutionlarge language modelsclarification questionscloud computinginteractive synthesisdisambiguation framework

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The pith

A disagreement-driven framework resolves underspecified natural language requests into accurate cloud IaC by asking questions about resources, topology, and attributes.

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

The paper establishes that cloud infrastructure code generation from vague user prompts can be improved by breaking ambiguity into three levels: which resources to use, how they connect in a topology, and what attributes each has. It introduces a method that creates multiple possible configurations, finds where they differ most informatively, and turns those differences into user questions. This interactive narrowing happens without retraining the language model and addresses the fact that IaC cannot be cheaply tested or fixed like regular code. On a new set of 300 ambiguous tasks, the approach lifts performance on structure matching by 18.4 percent and attribute matching by 25.4 percent relative to the best existing method.

Core claim

We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower-level ones. We propose a training-free, disagreement-driven framework that generates diverse candidate specifications, identifies structural disagreements across these axes, ranks them by informativeness, and produces targeted clarification questions that progressively narrow the configuration space.

What carries the argument

A training-free disagreement-driven disambiguation process that generates candidate IaC specifications and uses disagreements on the three axes of resources, topology, and attributes to rank and pose clarification questions.

If this is right

The method enables effective one-shot IaC generation despite expensive repairs by using interactive clarification.
Performance gains on structure and attribute evaluations indicate better handling of hierarchical constraints in configurations.
The framework can rank disagreements to focus questions on the most uncertain parts first.
Validated on 300 tasks, it provides a benchmark for future ambiguity resolution in IaC.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This approach could extend to other domains like ambiguous database schema design or network configuration where similar hierarchical decisions apply.
Integrating the clarification loop into existing LLM tools might lower the barrier for non-experts to manage cloud resources.
Further work could explore automating some questions via additional data sources to reduce user burden.

Load-bearing premise

Ambiguity in IaC prompts has a compositional structure across resources, topology, and attributes that higher-level choices constrain.

What would settle it

Running the framework on the Ambig-IaC benchmark and finding no improvement in graph edit distance or embedding similarity scores compared to a non-interactive baseline.

Figures

Figures reproduced from arXiv: 2604.02382 by Ang Chen, Hui Guan, Kaden Gruizenga, Patrick Tser Jern Kon, Tongyuan Miao, Zhenning Yang.

**Figure 2.** Figure 2: Overview of the iterative multi-level disambiguation process for interactive [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Per-round dynamics. RQ2. Most methods benefit from additional rounds, as shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Regeneration analysis. RQ5. Without round-robin (“Ours w/o RR”), disagreements are selected purely by entropy, making this ablation the closest analogue in our study to prior active task disambiguation methods that prioritize clarifications by information gain alone (Kobalczyk et al., 2025). In IaC, however, ambiguity is structured across resource composition, topology, and attributes, so a flat entrop… view at source ↗

read the original abstract

The scale and complexity of modern cloud infrastructure have made Infrastructure-as-Code (IaC) essential for managing deployments. While large Language models (LLMs) are increasingly being used to generate IaC configurations from natural language, user requests are often underspecified. Unlike traditional code generation, IaC configurations cannot be executed cheaply or iteratively repaired, forcing the LLMs into an almost one-shot regime. We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower-level ones. We propose a training-free, disagreement-driven framework that generates diverse candidate specifications, identifies structural disagreements across these axes, ranks them by informativeness, and produces targeted clarification questions that progressively narrow the configuration space. We introduce \textsc{Ambig-IaC}, a benchmark of 300 validated IaC tasks with ambiguous prompts, and an evaluation framework based on graph edit distance and embedding similarity. Our method outperforms the strongest baseline, achieving relative improvements of +18.4\% and +25.4\% on structure and attribute evaluations, respectively.

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.

Desk Editor's Note private letter to a colleague

The paper gives a training-free interactive clarification method for ambiguous IaC prompts based on multi-level disagreements, backed by a new benchmark, but the key hierarchy assumption lacks independent validation.

read the letter

This paper introduces a training-free method that generates multiple IaC candidates from an ambiguous prompt, detects disagreements across resources, topology, and attributes, ranks potential questions by informativeness, and narrows the space through targeted clarification. They back it with the Ambig-IaC benchmark of 300 tasks and report relative gains of 18.4% on structure and 25.4% on attributes using graph edit distance and embedding similarity over the strongest baseline. The focus on IaC is useful because errors there are costly and hard to iterate on cheaply, unlike regular code generation. The disagreement-driven ranking avoids training and gives a concrete way to prioritize what to ask the user. The evaluation framework with graph and embedding metrics fits the domain. The main soft spot is the central assumption that ambiguities decompose cleanly into that three-level hierarchy where higher decisions constrain lower ones. The paper treats this as an observation from their tasks, but there is no separate check such as inter-annotator agreement rates or counter-example analysis showing how often real prompts follow the structure outside their curation. If many cases violate it, the disagreement step may not reduce the space reliably and the reported gains could shrink. The abstract also leaves out details on baseline selection and statistical significance, though the full paper may address some of that. This is relevant for researchers and engineers building LLM tools for cloud configuration and infrastructure code. Readers working on interactive disambiguation or domain-specific synthesis would find usable ideas in the method and benchmark. It deserves peer review because it ships a concrete framework and dataset for a practical problem, even if the authors need to add validation for the hierarchy claim.

Referee Report

3 major / 2 minor

Summary. The paper introduces Ambig-IaC, a training-free, disagreement-driven framework for multi-level disambiguation in interactive IaC synthesis from natural language prompts. It posits that IaC configurations decompose into hierarchical axes of resources, topology, and attributes, enabling the generation of diverse candidates, identification of structural disagreements, ranking by informativeness, and targeted clarification questions. A benchmark of 300 validated ambiguous IaC tasks is presented, with evaluation showing relative improvements of +18.4% on structure and +25.4% on attribute metrics over the strongest baseline using graph edit distance and embedding similarity.

Significance. If the central claims hold, the work could meaningfully advance the practical use of LLMs for IaC by addressing underspecification through interactive, training-free methods, which is valuable given the high stakes of cloud infrastructure errors. The introduction of the Ambig-IaC benchmark and the compositional hierarchy approach represent potential contributions, though their impact hinges on rigorous validation of the hierarchy assumption and evaluation robustness.

major comments (3)

Abstract: The reported relative improvements of +18.4% and +25.4% on structure and attribute evaluations are presented without baseline absolute scores, statistical significance tests, or confidence intervals, making it impossible to determine whether the gains exceed noise or baseline strength.
§3 (method description): The claim that IaC ambiguity exhibits a tractable compositional structure with three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower ones is load-bearing for the disagreement identification step, yet the manuscript provides no independent validation such as inter-annotator agreement on axis constraints or counter-example rate outside the authors' task curation.
Evaluation framework (described in §4): The graph-edit-distance and embedding-similarity metrics are defined at a high level only; no analysis is given of how these metrics correlate with practical IaC outcomes such as successful deployment or user-perceived correctness, weakening the link between reported gains and real-world utility.

minor comments (2)

Abstract: The term IaC is introduced without spelling out Infrastructure-as-Code on first use, which could be added for readers outside the subfield.
Benchmark description: The curation process for the 300 tasks is summarized but lacks explicit details on prompt sourcing, ambiguity injection method, or validation protocol, which would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and outline planned revisions to strengthen the manuscript.

read point-by-point responses

Referee: Abstract: The reported relative improvements of +18.4% and +25.4% on structure and attribute evaluations are presented without baseline absolute scores, statistical significance tests, or confidence intervals, making it impossible to determine whether the gains exceed noise or baseline strength.

Authors: We agree that absolute scores, significance tests, and confidence intervals are necessary for proper interpretation. In the revised manuscript we will add the absolute baseline and method scores for both metrics, report p-values from paired t-tests across the 300 tasks, and include 95% confidence intervals computed via bootstrapping. revision: yes
Referee: §3 (method description): The claim that IaC ambiguity exhibits a tractable compositional structure with three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower ones is load-bearing for the disagreement identification step, yet the manuscript provides no independent validation such as inter-annotator agreement on axis constraints or counter-example rate outside the authors' task curation.

Authors: The hierarchy is grounded in standard IaC modeling conventions and was applied consistently during benchmark construction. We will expand §3 and the benchmark section to report inter-annotator agreement rates on axis assignments from the expert curation process and to discuss observed counter-examples. A larger-scale independent validation study lies beyond the current scope but is noted as future work. revision: partial
Referee: Evaluation framework (described in §4): The graph-edit-distance and embedding-similarity metrics are defined at a high level only; no analysis is given of how these metrics correlate with practical IaC outcomes such as successful deployment or user-perceived correctness, weakening the link between reported gains and real-world utility.

Authors: We acknowledge the value of explicit correlation analysis. Graph edit distance directly quantifies structural differences that affect deployability. In the revision we will add a targeted analysis on a 50-task subset showing the relationship between metric improvements and successful Terraform deployment in a test environment, together with results from a small user study measuring perceived correctness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; training-free method relies on stated observation without self-referential reduction

full rationale

The paper describes a training-free, disagreement-driven framework grounded in an observed compositional structure of IaC ambiguity across three axes. No equations, fitted parameters, or predictions that reduce to inputs by construction are present. The benchmark is introduced as validated without evidence of self-construction that forces the reported gains. Performance claims (+18.4% and +25.4%) are empirical comparisons to baselines. This matches the default expectation of low or zero circularity for self-contained empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Central claim rests on the domain assumption that IaC ambiguity factors cleanly into three hierarchical axes and that disagreement ranking produces informative questions; no free parameters or new physical entities are introduced.

axioms (1)

domain assumption Ambiguity in IaC exhibits a tractable compositional structure decomposable into resources, topology, and attributes with higher-level decisions constraining lower-level ones.
Explicitly stated as an observation in the abstract.

invented entities (1)

Ambig-IaC benchmark no independent evidence
purpose: Validated set of 300 ambiguous IaC tasks for evaluation
Newly introduced in the paper; no independent evidence outside this work.

pith-pipeline@v0.9.0 · 5512 in / 1302 out tokens · 39280 ms · 2026-05-13T21:48:45.422612+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower-level ones.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

[1]

Chinmaya Andukuri, Jan-Philipp Fr¨anken, Tobias Gerstenberg, and Noah Goodman

Accessed: 2026-04-01. Chinmaya Andukuri, Jan-Philipp Fr¨anken, Tobias Gerstenberg, and Noah Goodman. STar- GATE: Teaching language models to ask clarifying questions. InFirst Conference on Language Modeling,

work page 2026
[2]

Aiopslab: A holistic framework to evaluate ai agents for enabling autonomous clouds, 2025b

Yinfang Chen, Manish Shetty, Gagan Somashekar, Minghua Ma, Yogesh Simmhan, Jonathan Mace, Chetan Bansal, Rujia Wang, and Saravan Rajmohan. Aiopslab: A holistic framework to evaluate ai agents for enabling autonomous clouds, 2025b. URL https://arxiv.org/ abs/2501.06706. Sam Davidson, Li Sun, Bhavana Bhasker, Laurent Callot, and Anoop Deoras. Multi-iac- eva...

work page arXiv
[3]

Zhongjun Ding, Yin Lin, and Tianjing Zeng

URL https://arxiv.org/abs/2509.05303. Zhongjun Ding, Yin Lin, and Tianjing Zeng. Ambisql: Interactive ambiguity detection and resolution for text-to-sql,

work page arXiv
[4]

URLhttps://arxiv.org/abs/2508.15276. Firefly. State of iac

work page arXiv
[5]

https://www.firefly.ai/state-of-iac-2025 ,

work page 2025
[6]

Accessed: 2026-04-01. Gartner. Gartner forecasts worldwide it spending to grow 10.8% in

work page 2026
[7]

https://www.channel-impact.com/ gartner-forecasts-worldwide-it-spending-to-grow-10-8-in-2026/ , February

work page 2026
[8]

Google Cloud

Accessed: 2026-04-01. Google Cloud. Gemini for Google Cloud,

work page 2026
[9]

Accessed: 2026-03-31

Substack post. Accessed: 2026-03-31. HashiCorp. What is infrastructure as code with terraform? https://developer.hashicorp. com/terraform/tutorials/aws-get-started/infrastructure-as-code , June 2025a. Ac- cessed: 2026-04-01. HashiCorp. Terraform, 2025b. URLhttps://developer.hashicorp.com/terraform. Katarzyna Kobalczyk, Nicolas Astorga, Tennison Liu, and M...

work page 2026
[10]

Patrick Tser Jern Kon, Jiachen Liu, Yiming Qiu, Weijun Fan, Ting He Lei Lin, Haoran Zhang, Owen M

URLhttps://arxiv.org/abs/2502.04485. Patrick Tser Jern Kon, Jiachen Liu, Yiming Qiu, Weijun Fan, Ting He Lei Lin, Haoran Zhang, Owen M. Park, George S. Elengikal, Yuxin Kang Ang Chen, Mosharaf Chowdhury, Myungjin Lee, and Xinyu Wang. Iac-eval: a code generation benchmark for cloud infrastructure-as-code programs. InProceedings of the 38th International Co...

work page arXiv
[11]

Microsoft

URLhttps://arxiv.org/abs/2208.05950. Microsoft. Microsoft copilot in Azure,

work page arXiv
[12]

Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer

URL https://azure.microsoft.com/en-us/ products/copilot. Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. AmbigQA: Answering ambiguous open-domain questions. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.),Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5783–5797, Online...

work page 2020
[13]

A mbig QA : Answering ambiguous open-domain questions

Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.466. URL https:// aclanthology.org/2020.emnlp-main.466/. Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, ChenXue Wang, Shichao Liu, and Qing Wang. Clarifygpt: A framework for enhancing llm-based code generation via requirements clarification.Proc. ACM Softw. Eng., 1(FSE), July

work page doi:10.18653/v1/2020.emnlp-main.466 2020
[14]

URLhttps://doi.org/10.1145/3660810

doi: 10.1145/ 3660810. URLhttps://doi.org/10.1145/3660810. Jingjia Peng, Yiming Qiu, Patrick Tser Jern Kon, Pinhan Zhao, Yibo Huang, Zheng Guo, Xinyu Wang, and Ang Chen. Automated lifting for cloud infrastructure-as-code programs. In2025 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps),

work page doi:10.1145/3660810
[15]

Sanidhya Vijayvargiya, Xuhui Zhou, Akhila Yerukola, Maarten Sap, and Graham Neubig

URLhttps://arxiv.org/abs/2404.00227. Sanidhya Vijayvargiya, Xuhui Zhou, Akhila Yerukola, Maarten Sap, and Graham Neubig. Interactive agents to overcome ambiguity in software engineering,

work page arXiv
[16]

Vijayvargiya, X

URL https: //arxiv.org/abs/2502.13069. Yiming Xiang, Zhenning Yang, Jingjia Peng, Hermann Bauer, Patrick Tser Jern Kon, Yiming Qiu, and Ang Chen. Automated bug discovery in cloud infrastructure-as-code updates with llm agents. In2025 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps),

work page arXiv
[17]

Cloud infrastructure management in the age of ai agents.SIGOPS Operating Systems Review, 2025a

Zhenning Yang, Archit Bhatnagar, Yiming Qiu, Tongyuan Miao, Patrick Tser Jern Kon, Yunming Xiao, Yibo Huang, Martin Casado, and Ang Chen. Cloud infrastructure management in the age of ai agents.SIGOPS Operating Systems Review, 2025a. doi: 10.1145/3759441.3759443. URLhttps://doi.org/10.1145/3759441.3759443. Zhenning Yang, Hui Guan, Victor Nicolet, Brandon ...

work page doi:10.1145/3759441.3759443
[18]

11 Preprint

URL https://arxiv.org/abs/2510.21903. 11 Preprint. Under review. A Evaluation Metrics This appendix provides formal definitions for the evaluation metrics. The evaluation pipeline has two stages: (1) construct graphs from specs and compute GED for the structure score, and (2) use the GED node alignment to compute embedding-based attribute similarity. A.1 ...

work page arXiv