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2026 05 06 design insights

Note

Exploratory note on architecture and open questions around scientific data processing. Not a stable product commitment.

Design Insights

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Model-centric pre-training and literature-scale mining impose different failure sensitivities. The pipeline therefore treats semantic stability, structural consistency, and minability as explicit design targets, implemented mainly through Processor (quality) and Designer (views and contracts). Executable detail on pipelines, tags, and acceptance tests now lives in those product docs rather than in this essay.

2026 05 06 research review

Note

Exploratory note on architecture and open questions around scientific data processing. Not a stable product commitment.

Research Review

Scientific papers encode terminology, numerals, notation, figures, and qualifiers in ways that general-purpose text tools rarely preserve. Once PDF or OCR pipelines emit machine-readable text, small formatting defects propagate into training corpora, extractors, and review tools—so preparation standards must be domain-aware, not merely “spell-checked.”

Mapping the pipeline to the docs

This documentation splits operational detail (how to run and validate) from conceptual pages (contracts, layout, governance). This note is a map, not a tutorial: it tells you where to read once you know which stage you care about.