> For the complete documentation index, see [llms.txt](https://concept.mosaic-program.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://concept.mosaic-program.org/appendix/appendix-c-why-modular-research-provides-necessary-scaffolding-for-ai-in-science.md).

# Appendix C: Why Modular Research Provides Necessary Scaffolding for AI in Science

The case for modular, machine-readable research has a long lineage, including the FAIR (Findable, Accessible, Interoperable, Reusable) data initiative and the Nanopublications project. Progress in AI-for-science capabilities is leading to a resurgence of interest in these ideas, with recent initiatives more explicitly geared towards supporting powerful agentic AI. Examples include Open eXchange Architecture, OpenEval and the Agent-Native Research Artifact (ARA)

In particular, as Rowan Cockett argues in [Standards Matter More Than Ever in the Age of AI](https://oxa.dev/articles/scientific-standards-in-the-age-of-ai), AI coding tools work because code lives in a richly structured environment: version control, dependency graphs, type systems, tests, executable artifacts, and decades of accumulated tooling. "If we had jumped directly from Windows Notepad to chat-based code generation — skipping IDEs, compilers, and tooling — the result would have been stunted." AI for science is currently being asked to reason over PDFs: the equivalent of building modern software tooling on plain-text files alone.

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