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Preprint . 2026
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ZENODO
Preprint . 2026
License: CC BY
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Preprint . 2026
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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Wanderland: An Executable Knowledge Graph for AI-Native Documentation

Authors: Fawcett, Graeme;

Wanderland: An Executable Knowledge Graph for AI-Native Documentation

Abstract

We present Wanderland, an executable knowledge graph that treats structured markdown as a programming language. Unlike systems that extract knowledge graphs from documents or generate code from documentation, Wanderland makes the document itself the computational substrate. Fenced code blocks execute as functions, navigation constitutes query execution, and a multi-level cache implements compilation semantics. The architecture exhibits strong structural parallels to fifty years of research in compilers (streams with relocations), databases (query plans with operators), and distributed systems (event sourcing with projections). We argue that these parallels are not merely analogical but reflect shared computational invariants: the same design patterns that optimize binary linking can be adapted to optimize document rendering. A novel three-state provenance system (unverified → reviewed → verified) with hash-based drift detection provides continuous human verification without external tooling

Keywords

Model Context Protocol, provenance tracking, lisp-in-a-trenchcoat-made-of-prose, MCP, homoiconicity, exectuable knowledge graph, literate programming, knowledge management, AI-native documentation, document-as-code

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green