What the study found
The study presents a Theory-to-Module Conversion Protocol for turning selected features of a theory into a conditional detection module rather than treating the theory as a worldview to accept or reject. It describes a package that includes operational files, a SKILL.md pilot file, and several protocols for decomposition, boundary testing, module construction, and routing.
Why the authors say this matters
The authors say the framework is meant to support philosophy-to-module conversion, religion-to-module conversion, ethics, politics, AI alignment theory conversion, governance framework conversion, and ideology-capture prevention. The study suggests this approach should not replace domain expertise or trivialize religion, philosophy, ethics, or politics, and should not treat converted modules as final truth.
What the researchers tested
The paper describes an AI-readable package that decomposes the canonical SΔϕ-63 paper into files for AI ingestion and use. It includes a core declaration, AI quickstart, minimal prompt, theory-to-module schema, theory decomposition protocol, conditional detection module protocol, scope and boundary tests, overgeneralization and ideology-capture tests, a module output template, module composition and routing, do-not-use conditions, failure modes, a relation map, metadata, citation file, DOI references, license, and manifest.
What worked and what didn't
According to the abstract, the protocol lets an AI agent recognize when theory-to-module conversion should be invoked and apply a procedure for extracting a theory's root proposition, detected pattern, activation condition, input trace requirement, output classification, revealed and hidden cost structures, blind spots, overbinding risk, failure modes, unresolved model residue, revision path, and downstream SΔϕ modules. It also says the framework does not claim all theories are merely tools, does not erase the historical depth of theories, and does not replace domain expertise.
What to keep in mind
The abstract does not report empirical evaluation results, performance metrics, or comparative testing outcomes. It also does not describe limitations beyond the stated scope boundaries and the note that converted modules are not final truth.
Key points
- The paper proposes converting selected features of a theory into a conditional detection module.
- The package includes SKILL.md and multiple files for theory decomposition, boundary testing, and routing.
- The authors say the framework can be used for philosophy, religion, ethics, politics, AI alignment, and governance audits.
- The protocol includes unresolved model residue, revision path, failure modes, and downstream routing.
- The abstract says the framework does not replace domain expertise or treat converted modules as final truth.
Disclosure
- Research title:
- Theory-to-module protocol converts selected theories into conditional detection modules
- Image credit:
- Photo by olleaugust on Pixabay
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