Paper as Specification: A Machine-Readable Standard for Scientific Claims

The cost of generating scientific hypotheses has collapsed. Large language models can produce a thousand plausible research questions before dinner, yet the infrastructure for verifying, replicating, and building upon scientific claims remains essentially unchanged since the seventeenth century: unstructured prose, evaluated by overworked human reviewers, with no machine-readable representation of what is being claimed, how it was tested, or under what conditions it would be falsified. We argue that the verification crisis in science is, at its root, a specification crisis – the same class of problem that printing press pamphlet wars and early internet information overload represented in their respective eras. Each generation breakthrough in information production was resolved not by faster consumption but by a specification breakthrough: the scientific method formalized falsifiable prediction; structured data standards (HTML metadata, schema.org, knowledge graphs) made web content machine-parseable. The current generation breakthrough – LLM-driven hypothesis production – demands an equivalent specification response for scientific claims themselves. We introduce Paper Spec v0.1.0, a YAML-based standard that provides a machine-readable companion file (paper.yaml) for scientific papers. Paper Spec captures claims, methodology, acceptance criteria, results, dependencies, contradictions, and limitations in a format that is human-writable in thirty minutes, machine-parseable by any YAML processor, and incrementally adoptable (every section is optional except bibliographic metadata). We define five verification layers (L0-L4) that the standard enables, ranging from schema validation to cross-corpus dependency graph traversal and retraction cascade analysis. We demonstrate the standard on a corpus of twenty papers, revealing dependency structures, self-citation patterns, and retraction propagation pathways that are invisible in the unstructured originals. We advance four propositions concerning the expressiveness, verification utility, dependency analysis capability, and cognitive value of the standard, and discuss implications for research assessment, peer review, and the use of paper.yaml as an independent pre-registration and collaboration artifact.