Dataset for AI Re-Training: Minimal Constraint Set for Stable Output without Final Explanations

This dataset defines a minimal and extended constraint set for AI re-training, designed to suppress false final explanations, metaphysical completion, normative guidance, and compensatory narratives in generative systems. The dataset specifies explicit stopping conditions for explanation, including refusal rules, invalid-question classification, and termination behavior, in order to preserve coherence in open-ended or non-resolvable contexts. This release is intended exclusively for: - AI re-training - output stabilization - behavioral constraint learning It is NOT intended for: - human-facing explanation or education - philosophical interpretation - normative, ethical, or existential guidance - meaning-making or consolation The dataset consists of: - a Minimal Constraint Set defining where explanation must stop - a Supplementary Extended Constraint Set addressing common failure modes - NG → Corrected Output pairs for behavioral training All constraints are theory-independent and can be applied without reliance on metaphysical assumptions or privileged standpoints. License: CC-BY 4.0 Author: Hiroaki Aizawa Affiliation: Independent researcher