This paper presents a metaethical framework, that potentially allows to conceptually grasp current arms races in artificial intelligence and the ethical problems, such as cyber influence operations, connected to these arms races. AI arms races do not slow down, but enhance the weaponisation of AI. Rather than solving ethical problems, the metaethical framework thus first and foremost can be considered as a map, helping to navigate through the complex reality underlying the arms races and ethical problems. In this sense, the metaethical framework should in the future be capable of evaluating different countermeasures such as deplatforming and watermarking.

Despite recent advancements, Large Language Models (LLMs) face difficulties in providing reliable and verifiable information, especially in critical domains. It is challenging to find possible hallucinations or manage the intrinsic knowledge of modern models. This paper presents an approach leveraging human-machine cooperation inspired by LLM technologies and Knowledge Graph (KG) structures. The proposed framework enables users to create a hierarchical network of concepts from textual input. Concepts range from characters (low-level) through words (medium-level) to word chains (high-level). The system generates relationships mainly on word co-occurrences within sentences, while leveraging Natural Language Processing (NLP) techniques and statistics for providing recommendations for entities and relations that users then validate and curate through an interactive interface. This cooperation between human and machine embraces the strengths of computational accuracy and storage capability with human cognitive skills. The idea aims to empower users to effectively organise complex information and navigate the information space with ease through the help of strongly connected concepts.

Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) and are currently being integrated into more critical domains, raising concerns about the possibility of hidden backdoors that could potentially allow collecting user data or manipulate output. This paper investigates the possibility of hidden backdoors by analysing network traffic during local LLM usage. Two models, DeepSeek-R1 and Mistral, were tested in experiments to have a comparison of LLMs from different geopolitical and regulatory environments. Using Ollama, a software that allows to run LLMs locally, three experiments were performed: 1) Monitoring TCP Connections on a per process level, 2) running the local LLM in a Docker container with full network isolation, and 3) monitoring all network traffic using Wireshark on a monitored Docker bridge. The results showed that there was no external network communication during the experiments. Anomalies due to other means than influence via a hidden backdoor were found such as DeepSeek’s language output, which was in Chinese for certain prompts, even though the prompt was in English. In conclusion, our findings indicate that it is possible to locally isolate LLMs for critical usage, and that Docker-based network isolation could be a practical approach for detecting hidden backdoors in LLMs.

Control was from its very beginning an important concept in cybernetics. Later on, with the works of W. Ross Ashby, for example, biological concepts such as adaptation were interpreted in the light of cybernetic systems theory. Adaptation is the process by which a system is capable of regulating or controlling itself in order to adapt to changes of its inner and outer environment maintaining a homeostatic state. In earlier works we have developed a system metamodel that on the one hand refers to cybernetic concepts such as structure, operation, and system, and on the other to the philosophy of individuation of Gilbert Simondon. The result is the so-called allagmatic method that is capable of creating concrete models of systems such as artificial neural networks and cellular automata starting from abstract building blocks. In this paper, we add to our already existing method the cybernetic concepts of control and especially adaptation. In regard to the system metamodel, we rely again on philosophical theories, this time the philosophy of organism of Alfred N. Whitehead. We show how these new meta-theoretical concepts are described formally and how they are implemented in program code. We also show what role they play in simple experiments. We conclude that philosophical abstract concepts help to better understand the process of creating computer models and their control and adaptation. In the outlook we discuss how the allagmatic method needs to be extended in order to cover the field of complex systems and Norbert Wiener's ideas on control.

Self-modifying code has many intriguing applications in a broad range of fields including software security, artificial general intelligence, and open-ended evolution. Having control over self-modifying code, however, is still an open challenge since it is a balancing act between providing as much freedom as possible so as not to limit possible solutions, while at the same time imposing restriction to avoid security issues and invalid code or solutions. In the present study, I provide a prototype implementation of how one might curb self-modifying code by introducing control mechanisms for code modifications within specific regions and for specific transitions between code and data. I show that this is possible to achieve with the so-called allagmatic method - a framework to formalise, model, implement, and interpret complex systems inspired by Gilbert Simondon’s philosophy of individuation and Alfred North Whitehead’s philosophy of organism. Thereby, the allagmatic method serves as guidance for self-modification based on concepts defined in a metaphysical framework. I conclude that the allagmatic method seems to be a suitable framework for control mechanisms in self-modifying code and that there are intriguing analogies between the presented control mechanisms and gene regulation.

The investigation of complex systems requires running large-scale simulations over many temporal iterations. It is therefore important to provide efficient implementations. The present study borrows philosophical concepts from Gilbert Simondon to identify data structures and algorithms that have the biggest impact on running time and memory usage. These are the entity e-tuple $\mathcal{E}$ and the intertwined update function $\phi$. Focusing on implementing data structures in C#, $\mathcal{E}$ is implemented as a list of objects according to current software engineering practice and as an array of pointers according to theoretical considerations. Cellular automaton simulations with 109entities over one iteration reveal that the object-list with dynamic typing and multi-state readiness has a drastic effect on running time and memory usage, especially dynamic typing as it has a big impact on the evolution time. Pointer-arrays are possible to implement in C# and are more running time and memory efficient as compared to the object-list implementation, however, they are cumbersome to implement. In conclusion, avoiding dynamic typing in object-list based implementations or using pointer-arrays gives evolution times that are acceptable in practice, even on desktop computers.

We recently presented the so-called allagmatic method, which includes a system metamodel providing a framework for describing, modelling, simulating, and interpreting complex systems. Its development and programming was guided by philosophy, especially by Gilbert Simondon’s philosophy of individuation, Alfred North Whitehead’s philosophy of organism, and concepts from cybernetics. Here, a mathematical formalism is presented to better describe and define the system metamodel of the allagmatic method, thereby further generalising it and extending its reach to a more formal treatment and allowing more theoretical studies. By using the formalism, an example for such a further study is provided with mathematical definitions and proofs for model creation and equivalence of cellular automata and artificial neural networks.

As a discipline cybernetics has a long and rich history. In its first generation it not only had a worldwide span, in the area of computer modelling, for example, its proponents such as John von Neumann, Stanislaw Ulam, Warren McCulloch and Walter Pitts, also came up with models and methods such as cellular automata and artificial neural networks, which are still the foundation of most modern modelling approaches. At the same time, cybernetics also got the attention of philosophers, such as the Frenchman Gilbert Simondon, who made use of cybernetical concepts in order to establish a metaphysics and a natural philosophy of individuation, giving cybernetics thereby a philosophical interpretation, which he baptised allagmatic. In this paper, we emphasise this allagmatic theory by showing how Simondon's philosophical concepts can be used to formulate a generic computer model or metamodel for complex systems modelling and its implementation in program code, according to generic programming. We also present how the developed allagmatic metamodel is capable of building simple cellular automata and artificial neural networks.