Related Work

Executable Knowledge Graphs

Recent work on Executable Knowledge Graphs (xKG) [1] constructs graphs that integrate conceptual nodes with runnable code components extracted from papers and GitHub repositories. The system enables agents to both retrieve and execute methods when replicating AI research. xKG employs a hierarchical structure with Paper Nodes, Technique Nodes, and Code Nodes connected by structural and implementation edges.

Distinction: xKG extracts executable graphs FROM existing artifacts through an automated pipeline. Wanderland authors executable graphs AS the primary substrate. The graph is not constructed—it is the authoring medium itself.

Literate Programming and LLMs

The "Renaissance of Literate Programming in the Era of LLMs" [2] proposes Interoperable Literate Programming (ILP), combining code with natural language documentation to help LLMs better understand and generate code. The approach addresses challenges LLMs face with complex interdependencies in large codebases.

Distinction: ILP treats prose as guidance for code generation—documentation helps LLMs write better code. Wanderland treats prose as computation—there is no separate code to generate because the documentation itself executes.

Document-to-Graph Pipelines

Docs2KG [3] enables knowledge graph extraction from heterogeneous documents including emails, PDFs, web pages, and spreadsheets. The system uses dual-path processing (image conversion and markdown conversion) to recover structure from documents not written with structure in mind.

Distinction: Docs2KG recovers structure through ETL. Wanderland imposes structure at authoring time. The graph is not extracted from documents—documents are authored directly into the graph.

Notebook Provenance

Loops [4] is a JupyterLab extension that captures provenance information to visualize the impact of changes within notebooks. The system traces notebook evolution over time and highlights differences between versions across code, markdown, tables, and visualizations.

Distinction: Loops visualizes provenance after the fact for reproducibility research. Wanderland embeds provenance into the substrate with inline status indicators (🔴→🟡→🟢). The verification state is always visible, not reconstructed.

Model Context Protocol

MCP [5] is an open standard for AI systems to integrate with external tools, systems, and data sources. The protocol defines tools with structured descriptions, execution semantics, and security boundaries. As of 2025, MCP has been adopted as the de-facto standard for agent-tool integration.

Distinction: Standard MCP maintains separation between tool definitions and documentation about tools. Wanderland collapses this—the fenced code block IS the tool definition, IS the documentation, IS the implementation. No second representation requires synchronization.

Computational Notebooks

Jupyter [6] pioneered the computational notebook paradigm—interleaved markdown cells and code cells with inline output. Observable [7] extended this model with reactive dataflow: cells automatically re-execute when dependencies change. Both systems have become standard tools for data science and exploratory programming.

Distinction: Notebooks are code-first with documentation as annotation. Wanderland is documentation-first with code as semantic enrichment. In Jupyter, the .ipynb is a JSON blob containing code; in Wanderland, the .md file IS the source of truth for both prose and computation. More fundamentally, notebooks compute vertically (cell-by-cell execution order) while Wanderland computes hierarchically (section-based AST traversal).

Block-Based Authoring

Notion [8] and similar tools (Coda, Craft) popularized block-based document editing where pages compose atomic blocks: text, databases, embeds, code snippets. Each block has a type and can be nested, creating implicit document structure.

Distinction: Block editors provide structured authoring but blocks don't execute—a code block in Notion is syntax-highlighted text, not a runnable function. Wanderland fences execute. Additionally, Notion's structure is UI-driven; Wanderland's structure is text-driven (standard markdown remains readable without the system).

Docs-as-Code

The docs-as-code movement [9] applies software engineering practices to documentation: version control, CI/CD pipelines, automated testing, pull request reviews. Tools like MkDocs, Docusaurus, and Sphinx treat docs as source code artifacts.

Distinction: Docs-as-code uses code workflows FOR documentation. Wanderland makes documentation INTO code. The distinction is substrate vs. process: docs-as-code says "treat docs like code" (review them, test them, version them); Wanderland says "docs ARE code" (they literally execute).

Summary

Approach	Relationship to Documents	Wanderland Position
xKG	Extract graphs from papers	Author graphs as substrate
ILP	Prose guides code generation	Prose is computation
Docs2KG	ETL recovers structure	Structure at authoring time
Loops	Visualize provenance post-hoc	Embed provenance inline
MCP	Separate tool defs from docs	Tool def is doc is impl
Jupyter/Observable	Code-first with prose annotation	Prose-first with code enrichment
Notion/Block editors	Structured UI, inert code blocks	Text-driven, executable fences
Docs-as-code	Code workflows for docs	Docs that ARE code

External References

citations:
  - id: 1
    title: "Executable Knowledge Graphs for Replicating AI Research"
    authors: [Chen et al.]
    venue: arXiv
    year: 2025
    url: https://arxiv.org/abs/2510.17795
    status: unverified
  - id: 2
    title: "Renaissance of Literate Programming in the Era of LLMs"
    authors: [TBD]
    venue: arXiv
    year: 2025
    url: https://arxiv.org/abs/2502.17441
    status: unverified
  - id: 3
    title: "Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents"
    authors: [TBD]
    venue: arXiv
    year: 2024
    url: https://arxiv.org/abs/2406.02962
    status: unverified
  - id: 4
    title: "Loops: Leveraging Provenance and Visualization to Support Exploratory Data Analysis in Notebooks"
    authors: [Eckelt, Gadhave, Lex, Streit]
    venue: IEEE VIS
    year: 2024
    url: https://vdl.sci.utah.edu/publications/2024_vis_loops/
    status: unverified
  - id: 5
    title: "Model Context Protocol Specification"
    authors: [Anthropic]
    venue: modelcontextprotocol.io
    year: 2024
    url: https://modelcontextprotocol.io/specification/2025-11-25
    status: unverified
  - id: 6
    title: "Project Jupyter"
    authors: [Jupyter Development Team]
    venue: jupyter.org
    year: 2014-present
    url: https://jupyter.org/
    note: Computational notebook standard
  - id: 7
    title: "Observable: The JavaScript Notebook"
    authors: [Bostock, M. et al.]
    venue: observablehq.com
    year: 2018-present
    url: https://observablehq.com/
    note: Reactive dataflow notebooks
  - id: 8
    title: "Notion: The All-in-One Workspace"
    authors: [Notion Labs]
    venue: notion.so
    year: 2016-present
    url: https://notion.so/
    note: Block-based document authoring
  - id: 9
    title: "Docs as Code"
    authors: [Gentle, A.]
    venue: Write the Docs
    year: 2017
    url: https://www.writethedocs.org/guide/docs-as-code/
    note: Documentation engineering methodology

North

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- slug: wanderland-paper
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  - Paper parent to related work section

East

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- slug: wanderland-sota-assessment
  context:
  - Detailed comparison with external validation
- slug: wanderland-paper-system-design
  context:
  - Section sequence

West

slots:
- slug: wanderland-paper-introduction
  context:
  - Previous section

Provenance

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