Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji, Rafael Ferreira da Silva, Wesley Brewer, Valentine Anantharaj, Sandro Fiore, Renan Souza
• Venue: arXiv: cs.DC
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.11022v1
• PDF: https://arxiv.org/pdf/2608.11022v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper introduces Workflow Cards, a new type of structured summary for machine learning artifacts that focus on workflow executions, which are currently overlooked in Model and Data Cards. Workflow Cards aim to provide crucial details about data preparation, parameter choice, runtime behavior, resource use, and intermediate transformations, where issues like bias and reproducibility often arise. The paper consists of two parts: first, it outlines a template for Workflow Cards based on provenance questions derived from execution-level data. Second, it compares the effectiveness of large language models (LLMs) in understanding workflow executions using Workflow Cards versus schema-based querying.

What the Document Says

• Model and Data Cards have been useful for capturing context, parameters, limitations, and intended use of machine learning artifacts. However, they overlook workflow executions that produce, transform, and evaluate the artifacts.
• Workflow executions contain critical details about data preparation, parameter choice, runtime behavior, resource use, and intermediate transformations.
• Bias, performance variation, and reproducibility gaps often originate from these execution-level details.
• The paper proposes Workflow Cards to fill this gap by condensing the machine-readable provenance data of a workflow execution into a form both humans and large language models can read and analyze.
• The paper consists of two main parts: defining a Workflow Card template and evaluating the effectiveness of LLMs in understanding workflow executions using Workflow Cards versus schema-based querying.

Key Concepts Mentioned

• Model Cards
• Data Cards
• Workflow Executions
• Provenance Data
• Large Language Models (LLMs)
• Workflow Cards
• Template
• Schema-based Interface

Claims Made by the Document

• Workflow executions hold critical details about data preparation, parameter choice, runtime behavior, resource use, and intermediate transformations.
• Workflow Cards provide execution-level information absent from existing card types.
• Workflow Cards nearly double answer quality compared with schema-based querying.

Evidence Used

• The text discusses the importance of workflow executions and their missing representation in Model and Data Cards.
• The text describes the benefits of Workflow Cards, including providing execution-level information not present in existing card types.
• The text presents results showing that Workflow Cards improve answer quality compared with schema-based querying.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.