SΔϕ-62 — World Model Kernel: Observed Trace, Inference, UMR, Binding Status, and Revision Path Protocols (v1.1, AI-Readable Kernel Package)

Abstract

SΔϕ-62 defines the World Model Kernel within the Sofience–Δϕ Formalism Series. The central claim is that AI inference should not collapse observed trace, inference, assumption, unresolved model residue (UMR), binding status, and revision path into a single surface answer. Before producing a conclusion, an AI should be able to state what was observed, what was inferred, what remains unresolved, how strongly the claim is bound to the world, and what would revise it. This AI-readable kernel package decomposes the canonical SΔϕ-62 paper into operational files for AI ingestion, claim decomposition, inference grounding, hallucination risk reduction, source-sensitive reasoning, world-binding assessment, UMR preservation, and multi-module SΔϕ routing. It includes the canonical paper, core declaration, AI quickstart, minimal prompt, world model kernel schema, trace/inference/UMR protocol, binding status rubric, revision path protocol, hallucination diagnostic module, world-binding test, claim decomposition protocol, output templates, do-not-use conditions, failure modes, relation map, kernel routing priority, metadata, citation file, DOI references, license, and manifest. The package is designed as a low-cost reasoning bootloader for AI systems. It is intended to be applied before downstream SΔϕ modules such as body-cost return, disclosure terrain, transition governance, transition completion cost, lent thought, group cost attribution, affective cost orientation, utilitarian subject-splitting, sacred-authority cost terrain, theory-to-module conversion, language as temporary fixation, and slop as externalized restabilization cost. The framework is not an excuse to avoid answering, not epistemic relativism, not a hallucination label generator, not a final truth detector, and not a denial of lived reports. It enables calibrated answers by distinguishing direct trace from inference, preserving uncertainty without collapsing it into falsehood, identifying overbinding and underbinding, and requiring revision paths for claims. The package is intended for AI inference grounding, claim decomposition, hallucination risk reduction, world-binding assessment, UMR preservation, revision path generation, source-sensitive reasoning, fact/inference separation, and multi-module SΔϕ routing.

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