---
格式版本: 2
标题: "Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data"
原文链接: "https://arxiv.org/abs/2608.11022"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:19:50.236Z"
来源平台: "arXiv 学术论文搜索"
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去重键: "https://arxiv.org/abs/2608.11022"
---

## Computer Science > Distributed, Parallel, and Cluster Computing

## Title:Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data

[View PDF](https://arxiv.org/pdf/2608.11022) [HTML (experimental)](https://arxiv.org/html/2608.11022v1)

> Abstract:Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations, and intended use. However, these practices remain focused on static artifacts (the datasets and trained models themselves) while overlooking the workflow executions that produce, transform, and evaluate them. Such executions hold critical details about data preparation, parameter choice, runtime behavior, resource use, and intermediate transformations, precisely where bias, performance variation, and reproducibility gaps tend to originate. To close this gap, we introduce Workflow Cards: structured summaries that condense the machine-readable provenance data of a workflow execution into a form both humans and large language models (LLMs) can read and analyze. This paper has two main parts. First, it defines a Workflow Card template informed by a representative set of provenance questions that surface from the execution-level data missing from Model and Data Cards. Second, it evaluates how effectively LLMs use Workflow Cards to understand workflow executions compared with querying provenance databases through a schema-based interface. Results show that Workflow Cards provide execution-level information absent from existing card types, such as Model Cards and Data Cards, thereby filling an important documentation gap; and that Workflow Cards nearly double answer quality compared with schema-based querying, consistently across LLM-as-a-Judge and human assessments.

| Comments: |  |
| --- | --- |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) |
| Cite as: | [arXiv:2608.11022](https://arxiv.org/abs/2608.11022) \[cs.DC\] |
|  | (or [arXiv:2608.11022v1](https://arxiv.org/abs/2608.11022v1) \[cs.DC\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11022](https://doi.org/10.48550/arXiv.2608.11022) |

## Submission history

From: Gabriele Padovani \[[view email](https://arxiv.org/show-email/22ca3280/2608.11022)\]  
**\[v1\]** Tue, 11 Aug 2026 15:02:11 UTC (2,063 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11022) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
