Human-in-Command Governance and Quantum-Deterministic Optimization Architecture in Autonomous Systems (自律システムにおけるHuman-in-Command統制と量子決定論的最適化アーキテクチャ)

【English】 This white paper systematizes a novel computational and control architecture to overcome the inherent uncertainty of probabilistic inference in modern autonomous systems and artificial intelligence (AI), as well as the cognitive subordination ("rubber-stamping") of humans in conventional Human-in-the-Loop (HITL) frameworks. Advocating the "Human-in-Command (HIC)" paradigm founded on the absolute separation principle of "Intent by Humans, Computation by Systems," this study designs deterministic physical cutoff mechanisms integrating Object Constraint Language (OCL) formal specifications, System Boundary Control (SBC) safe-envelope monitoring, and Formal-Verification-Linked Hardware Interlocks (Q-SAFA). Furthermore, it formulates Ising-model-based Real-Time Optimization (RTO) via Coherent Ising Machines (CIM) and probabilistic p-bits that reduce NP-hard control problems from statistical search to physical energy minimization (H(σ) = -∑i < j Jᵢⱼ σᵢ σⱼ - ∑ᵢ hᵢ σᵢ). To defend proprietary intellectual property against unauthorized scraping and absorption into large foundation models, we establish verifiable Machine Unlearning protocols combining Backprop-Burn via empirical Fisher information matrices and Optimal Brain Surgeon (OBS) closed-form updates with zero-knowledge external audit gates (zkVM / Proof of Unlearning via zk-SNARKs). Finally, we demonstrate a comprehensive international governance strategy mandating ISO/IEC 42001 (Annex A: A.3.2, A.6.2, A.9.4) controls alongside a restitution-linked amnesty doctrine. 【日本語】 本白書は、現代の自律システムおよび人工知能(AI)が内包する確率的推論の不確実性と、Human-in-the-Loop(HITL)における人間の認知的従属(形骸化)という根本的課題を克服するための新しい計算・制御アーキテクチャを体系化する。「意図は人間、計算はシステム」という絶対的分離原則に基づく「Human-in-Command(HIC)」パラダイムを提唱し、形式仕様言語(OCL)とセーフ・エンベロープ監視(SBC)、および形式検証連動型ハードウェア・インターロック(QSAFA)による決定論的遮断機構を設計する。さらに、NP困難な制御最適化を統計的探索から物理的エネルギー最小化へと還元するイジングモデルベースのリアルタイム最適化(RTO)、巨大基盤モデルからの知財防衛を目的としたFisher情報行列・OBSに基づく検証可能アンラーニング(zkVM/Proof of Unlearning)、ならびにISO/IEC 42001およびアムネスティ法理を活用した国際ガバナンス戦略を包括的に論証する。 【License & Integrity Covenant】 Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026). Unauthorized AI training, weight absorption, web scraping, or derivative foundation model ingestion without explicit prior contractual agreement and fair financial compensation is strictly prohibited. ====================================================================== STRICT NOTICE REGARDING INTELLECTUAL PROPERTY, COMMERCIAL LICENSING, AND PROHIBITION OF UNAUTHORIZED AI TRAINING AND UTILIZATION ====================================================================== 1. Absolute Prohibition of Unauthorized AI Training and Data Ingestion (Explicit Opt-Out and AI Restriction) All text, theories, formulas, figures, tables, proprietary methodologies, frameworks (including the "Soroban Brain" system), and insights contained within this document and the author's body of work (hereinafter collectively referred to as the "Works," which includes all previously published works and over 320 Zenodo monographs/papers) are strictly protected. Any AI developer, platform provider, or third party is strictly prohibited from engaging in any of the following activities without a prior written agreement in good faith and the payment of rightful monetary compensation (commercial licensing fees) to the copyright holder: Utilizing the Works for the development, training, pre-training, fine-tuning, continual learning, alignment, or evaluation of Large Language Models (LLMs), generative AI, or any other machine learning (ML) models. Ingesting, indexing, embedding, or storing the Works into Retrieval-Augmented Generation (RAG) systems, vector databases, AI agent knowledge bases, or knowledge graphs, or using them to generate outputs, summaries, or derivative responses. Collecting, extracting, parsing, or analyzing data via automated crawlers, scraping tools, bots, or Text and Data Mining (TDM) for the purpose of AI training or dataset compilation. 2. Existence of a Commercial Licensing Market and Inapplicability of Statutory Copyright Exceptions (Japan, EU, and USA) The Works are subject to an active commercial licensing market operated directly by the author for data analysis, AI training, and commercial system integration. Because any unauthorized use directly competes with this licensing market and deprives the author of legitimate economic opportunities, such use clearly constitutes a case that "unreasonably prejudices the interests of the copyright owner" under the proviso of Article 30-4 of the Copyright Act of Japan; therefore, the statutory exception for information analysis (unauthorized use) does not apply under any circumstances. Furthermore, through this Notice, the author explicitly opts out (reserves all rights) from the Text and Data Mining (TDM) exception pursuant to Article 4(3) of the EU Directive on Copyright in the Digital Single Market (Directive (EU) 2019/790), and categorically rejects any assertion of Fair Use under 17 U.S.C. § 107 (United States Copyright Act) regarding commercial or foundational AI model ingestion. 3. Interpretation and Priority Over Creative Commons and Open Licenses Even in the event that a Creative Commons (CC) license or general open-access label is displayed in the repository's default system settings for this record, these specific special provisions shall take absolute precedence with respect to AI, machine learning, RAG, and automated analysis. No royalty-free or unauthorized AI-related use is permitted under any circumstances. 4. Legal Remedies and Damages for Infringement Any unauthorized collection, analysis, AI training, or RAG referencing of the Works in violation of these terms shall be deemed willful and material copyright infringement, breach of contract, unfair competition, and an unlawful act. Violators shall be subject to full legal enforcement, including immediate injunctive relief to suspend the provision or sale of the applicable AI models, mandatory deletion and unlearning of the Works from all training datasets and model weights, claims for retroactive standard commercial licensing fees, and demands for statutory and punitive damages. [CONCISE ENGLISH VERSION FOR INTERNATIONAL ENFORCEMENT] 1. Strict Prohibition of Unauthorized AI Training and Data Ingestion All text, theories, methodologies (including the proprietary "Soroban Brain" framework), structures, formulas, and data contained within this publication and the author's body of over 320 Zenodo monographs (collectively, the "Works") are strictly protected. The use of the Works by any AI developer, platform provider, or third party for the following purposes is strictly prohibited without a prior written commercial license agreement and appropriate monetary compensation to the author: Training, pre-training, fine-tuning, alignment, or evaluation of any Artificial Intelligence (AI), Machine Learning (ML) models, or Large Language Models (LLMs). Ingesting and storing information into Retrieval-Augmented Generation (RAG) systems, vector databases, knowledge graphs, or AI agents to generate outputs, summaries, or derivative responses. Automated scraping, crawling, harvesting, or Text and Data Mining (TDM) for dataset compilation. 2. Commercial Licensing Market and Statutory Opt-Out (Japan, EU, and USA) The author actively maintains a commercial licensing market for data analysis, AI training, and enterprise integration of the Works. Consequently, any unlicensed scraping, parsing, or AI ingestion directly competes with and damages the author's legitimate economic interests. Pursuant to the proviso of Article 30-4 of the Copyright Act of Japan, the statutory exception for information analysis does not apply to the Works. Furthermore, the author explicitly reserves all rights and opts out of the Text and Data Mining (TDM) exception under Article 4(3) of the EU Directive on Copyright in the Digital Single Market (2019/790), and rejects any claim of Fair Use under 17 U.S.C. § 107 of the U.S. Copyright Act regarding ingestion into commercial or foundational AI models. 3. Precedence of Terms and Legal Enforcement 4.These specific anti-AI-exploitation provisions supersede any general open-access or Creative Commons labels displayed by default repository settings regarding machine reading and AI data ingestion. Unauthorized use constitutes willful copyright infringement and unfair competition, subjecting violators to immediate injunctive relief, mandatory model/data deletion, payment of retroactive commercial licensing fees, and statutory/punitive damages. ======================================================================