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

arXiv: https://arxiv.org/abs/2608.11022 · HTML: https://mlsanity.com/paper/2608.11022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://mlsanity.com/paper/2512.20848/) (2025)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows](https://mlsanity.com/paper/2508.02866/) (2025)
- [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024)
- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [What's documented in AI? Systematic Analysis of 32K AI Model Cards](https://mlsanity.com/paper/2402.05160/) (2024)
- [Applying the FAIR Principles to computational workflows](https://mlsanity.com/paper/2410.03490/) (2024)
- [LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology](https://mlsanity.com/paper/2509.13978/) (2025)
- [Workflow Provenance in the Lifecycle of Scientific Machine Learning](https://mlsanity.com/paper/2010.00330/) (2020)
- [Recording provenance of workflow runs with RO-Crate](https://mlsanity.com/paper/2312.07852/) (2023)
- [Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability](https://mlsanity.com/paper/2308.09004/) (2023)
- [WorkflowHub: a registry for computational workflows](https://mlsanity.com/paper/2410.06941/) (2024)
- [Provenance Tracking in Large-Scale Machine Learning Systems](https://mlsanity.com/paper/2507.01075/) (2025)
- [yProv4ML: Effortless Provenance Tracking for Machine Learning Systems](https://mlsanity.com/paper/2507.01078/) (2025)
- [In-Memory Indexing and Querying of Provenance in Data Preparation Pipelines](https://mlsanity.com/paper/2511.03480/) (2025)
- [Provenance for Lattice QCD workflows](https://mlsanity.com/paper/2303.12640/) (2023)

## Abstract (arXiv)

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.