The NMC Theory for Global Bipolarity: A Structural Law of The Atomic Era
Nguyen, Cong
Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
By using an axiomatic method, this manuscript identifies the first structural law ever formulated in International Relations. It introduces a new ontology of global order and shows that the post-1945 system obeys a geometric constraint that permits only two true Poles. Appendices 1-3 focus on method which is new and helpful for further studies in social science. Appendices 2-3 combine appendix in Logic of Closure give us The Minimal Test. V4 adds a direction for future researches. V5: rewrite appendix 4 The companion paper (Logic of Closure) introduces the first computational epistemology for real-time structural verification, and The Minimal Test for validating any law or theory in social science. https://doi.org/10.5281/zenodo.17911012 V6: adds a truly ugly appendix 7 to clear some possible misunderstanding about grayzone, boundary,... structurally. V7: adds only Appendix 8 illustrating discussion of deep governance layers (norms and institutions). V8: adds Appendix 9 which answers a question about Venezuela-Greenland sequence in NMC context. A deeper discussion may start from section 5.2. Also adds Disclaimer, Methodology. V9: add Clairification for Appendix 9. V10: delete the last line in Section 7 + add Appendix 10, Notes. V11: adds Appendix 11 - the last one. The manuscript is done! Appendices 7, 9, 10 and 11 (application layer) can be applied to read moves of Poles in future. V12: add Appendix 12 to complete Appendix 11. Two True Poles can be considered as systemic nodes which create tides/forces, etc. Appendices 4, 7-12 are however not ontology/core of The NMC Theory. They are mainly operational layers. V13: just add Appendix 13 V14: add note for Appendix 9, 11, 13 + edit 11.2, 11.12, 11.13 Note: For operational layer without NMC, we have a trilogy (weaker versions of 6.2, appendices 7, 9, 11-13) Geometry of Closure: https://doi.org/10.5281/zenodo.21107689 Strategic Stability and End of Absolute Freedom: https://doi.org/10.5281/zenodo.21411649 Maintaining the System: https://doi.org/10.5281/zenodo.21273268 V15: almost remove 3.7, rename 6.2, reorganize 6.3, add new 6.4 On The Logical Upper Boundary of NMC - on Aug 8 2026. Note. Application for SEA and non-poles generally (how to survive): https://doi.org/10.5281/zenodo.21938856 V16: minor edit last lines of section 7 and add some notes for A11-13 Structural Survival Doctrine for Japan https://doi.org/10.5281/zenodo.22001535 V17: remove "counter pole" in the def of True Pole, edit Methodology, Abstract, note C3 as the foundational law, and add note about Structural Time for A11. The third paper (ethics/law for AI) is "THE GEOMETRY OF STRUCTURAL ETHICS: Cognitive Symmetry as a Constitutional Invariant of the Post-Human Epoch" https://doi.org/10.5281/zenodo.17831278 DISCLAIMER NMC is a descriptive structural law, not a normative doctrine. It predicts systemic trajectories under post-1945 closure, but it does not justify, legitimize, or morally endorse any policy conducted in its name. Structural inevitability is ethically neutral; the ethical burden lies in how actors exploit inevitability. NMC may explain why “deals,” coercive compromises, and structural accommodations emerge, yet it does not recommend them, nor does it equate equilibrium with justice. Methodology: The Axiomatic Construction of NMC The NMC theory is not an inductive synthesis of historical narratives but a formally constructed axiomatic system. Engagement with NMC begins with its internal logic prior to empirical application. 1. Formal Validity and Internal Consistency The first-order validity of NMC is strictly internal. As an axiomatic system, it is evaluated along two criteria: Partition Exhaustiveness: whether its ontology (True Poles, Pseudo-Poles, Gray Zones) provides a complete and domain-bounded classification of all actors in the post-1945 nuclear-technological era; Axiomatic Consistency: whether its fundamental axioms (e.g., Axiom Zero, twele axioms, the Law of Structural Closure) are mutually consistent and non-contradictory. If these criteria are satisfied, NMC constitutes an autonomous formal-mathematical space. Its logical integrity stands independently of normative preference. 2. Empirical Fitness as a Second-Order Filter Empirical history and contemporary events function as a second-order filter assessing the fitness of this formal structure to its intended domain. Trajectory Validation: When observed systemic dynamics conform to trajectories induced by the axioms (e.g., patterns of pole competition, structural absorption of pseudo-poles), this constitutes empirical support. Perturbation vs. Violation: Surface-level deviations or short-term political resistance are considered local perturbations within the established geometry. Such perturbations do not, in themselves, invalidate the axioms unless they demonstrate a genuine structural violation. 3. Standards for Refutation An internally consistent axiomatic system can be refuted only on the following grounds: Formal Grounds: uncovering an internal logical inconsistency or showing that the partition is not exhaustive; Structural-Empirical Grounds: providing a genuine counterexample that breaks the structural topology — e.g., the sustained emergence of a bona fide Third Pole that independently satisfies all closure requirements. Critiques rooted in moral judgments, normative “oughts,” or conventional geopolitical metaphors (such as multipolar balance of power) are methodologically irrelevant unless they explicitly engage with the structural laws governing this closed system. Note. NMC is formulated as a structural law governing the post-1945 closed system. It does not claim exclusivity over all social phenomena; rather, it specifies the structural topology within which IR-level events unfold.
Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation .
Malik Abdul Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 43 citations· ⚡2
Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 40 citations
The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.
Ville Vakkuri, P. Abrahamsson· International Conference on...· 39 citations· ⚡2
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.