The open knowledge base for Provenance Engineering in software systems.
NIST defines provenance as:
“The chronology of the origin, development, ownership, location, and changes to a system or system component and associated data.”
— NIST, Provenance — CSRC Glossary
W3C PROV provides a broader, domain-independent definition:
“Provenance is information about entities, activities, and people involved in producing a piece of data or thing.”
— W3C, PROV Family of Documents
Provenance Engineering is the discipline of designing systems that capture, preserve, verify and query the origins, identity, dependencies, transformations, authority and causal history behind digital artifacts and automated decisions.
| Paper | Year / Status | Provenance relevance | Link |
|---|---|---|---|
| Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering — Souza et al. | 2019 · Preprint | Introduces PROV-ML, extending W3C PROV to represent provenance across the machine-learning lifecycle | Paper |
| Capturing End-to-End Provenance for Machine Learning Pipelines — Schlegel & Sattler | 2025 · Information Systems | Captures interoperable end-to-end ML pipeline provenance and maps MLflow/Git activity to W3C PROV | Paper |
| Model Provenance Testing for Large Language Models — Nikolic, Baluta & Saxena | 2025 · Preprint | Tests whether one LLM was derived from another using black-box behavioral evidence | Paper |
| Agent-Sentry: Bounding LLM Agents via Execution Provenance — Sequeira et al. | 2026 · Preprint | Uses execution provenance as an active security primitive to detect and block out-of-bounds agent behavior | Paper |
| From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents — Wang et al. | 2026 · Preprint | Defines typed execution-provenance graphs for tracing evidence, tool calls, memory, actions and agent outputs | Paper |
| Operationalising Artificial Intelligence Bills of Materials for Verifiable AI Provenance and Lifecycle Assurance — Radanliev et al. | 2026 · Frontiers in Computer Science | Connects AI-BOMs, model lineage, lifecycle provenance and cryptographic verification for AI supply-chain assurance | Paper |
| A Survey of Machine Learning Lifecycle Provenance: Models, Approaches, and Tools — Vonderhaar, Procko & Ochoa | 2026 · Conference paper | Surveys ML provenance models, lifecycle approaches, standards and tooling | Paper |
| Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs — Hohensinner et al. | 2026 · Preprint | Surveys data provenance and traceability across LLM datasets and model-development pipelines | Paper |
| A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models — Erfan, Ryan & Rahman | 2026 · Preprint | Empirically measures provenance and lifecycle metadata completeness across large numbers of Hugging Face model artifacts | Paper |
| When Does Execution Provenance Help Agent Memory Retrieval? — Wang et al. | 2026 · Preprint | Studies provenance-aware memory structures for retrieving supporting evidence in agent workflows | Paper |
| Verifiable Provenance of Software Artifacts with Zero-Knowledge Compilation — Ron & Monperrus | 2026 · Preprint | Produces veriable source-code provenance leveraging Zero Knowledge compilation | Paper |