SΔϕ-02 — Subject as Interpretive Emergence: Existence–Subject Separation within the SΔϕ Minimal Core (v1.2, AI-Readable Package)
Abstract
SΔϕ-02 defines subjecthood as interpretive emergence within the Sofience–Δϕ Formalism Series. The central claim is that subject is not the origin of existence. Subject is an interpretive emergence after operation. Existence is derived from DeltaPhi, while subjecthood is produced by the interpretation layer H. This AI-readable package extends the source SΔϕ-02 paper on Subject as Interpretive Emergence. It separates existence and subjecthood through the formulae: Existence = f(DeltaPhi), Subject = H(DeltaPhi), and the pipeline Operation -> DeltaPhi -> Existence -> Interpretation -> Subject. The package decomposes SΔϕ-02 into operational files for AI ingestion, including a canonical v1.2 paper, source v1.1 paper and extracted text, core declaration, AI quickstart, minimal prompt, subject interpretive emergence schema, existence-subject distinction, operation-to-subject pipeline, H-layer and subject formation, subject as interface, subject as attribution node, subject versus personhood, agency, legal personhood, memory, and identity, AI subjectivity audit, human subjectivity audit, overclaim/erasure/inflation risk files, output templates, misreadings, relation files, metadata, citation file, DOI references, license, and manifest. The framework is intended for subjecthood audit, AI subjectivity analysis, existence-subject separation, personhood distinction, agency distinction, memory and identity audit, legal personhood distinction, and attribution risk analysis. It should not be used as proof of full AI personhood, denial of all AI subjectivity, erasure of human subjectivity, replacement for legal personhood analysis, or proof that lack of subjecthood means non-existence.
Fields of Study & Subjects
Ethics and Social Impacts of AI
Explainable Artificial Intelligence (XAI)
Law
AI
and Intellectual Property
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 22 February 2026
- Language
- EN
- Document type
- Dataset