Iterative Constraint Construction: A Replicable Human–AI Workflow for Structured Theoretical Development

Function in corpus

The methodological specification for the corpus's own construction process. Makes the human-AI workflow replicable by any researcher attempting similar constraint-disciplined theoretical development, and documents the governance architecture that kept the IO corpus coherent across 72 papers.

Details

How do you build a rigorous philosophical corpus without letting it drift? This paper formalizes the methodology used to construct Informational Ontology itself — a replicable human-AI workflow for developing constraint-disciplined theoretical systems that remain coherent across dozens of papers and hundreds of revision cycles. The workflow is constraint-first throughout. It begins with explicit primitive declaration (what the framework assumes as given) and explicit exclusions (what it refuses to explain), then proceeds through structured iterative expansion, adversarial pressure, and version-controlled amendment discipline. Every extension must be dependency-traceable back to declared primitives. Every revision must be classified — as extension, repair, or revision — and versioned. Silent changes are not permitted. A key architectural principle: the human retains all epistemic authority. Primitive declaration, scope enforcement, drift adjudication, amendment classification, and final stabilization are human functions. AI participation is operational — variation generator, adversarial simulator, large-scale consistency amplifier — not normative. • Connected papers: Informational Ontology; The Truth Protocol; Can AI Participate in Philosophical Method?

Availability

This paper is listed for orientation and dependency tracking. No public PDF or Zenodo record is linked yet.