This paper presents a multi-agent, NLP-driven task coordination framework that constitutes a practical architecture for Artificial General Intelligence (AGI). It leverages real-time agent-driven analysis, language-based task structuring, and LLM-powered iterative optimization to enhance adaptability in dynamic environments. Traditional static evaluation methods in multi-agent systems struggle with evolving task constraints and interdependencies, often leading to inefficiencies in decision-making and workload distribution. While existing multi-agent architectures have introduced task-sharing mechanisms, they remain rigid in dynamic execution scenarios, lacking real-time adaptation to agent constraints and evolving system states. To address these challenges, we introduce a hierarchical task allocation model that integrates a Centralized Control Unit (CCU) for system-wide orchestration, an NLP-based Dynamic Task Allocation (NLP DTA) module for context-aware task structuring, and distributed agents that iteratively refine execution through structured feedback loops. Unlike existing multi-agent coordination models, which rely on preprogrammed heuristics or static task allocation, our AGI framework enables agents to first self-assess task feasibility, allowing NLP DTA to construct an optimal execution plan. This enables iterative task reassignment, where LLM-driven analysis dynamically adjusts agent responsibilities based on real-time execution feedback. The proposed AGI architecture demonstrates strong scalability and adaptability, making it suitable for AI-driven applications such as autonomous cloud resource allocation, workflow optimization, and decentralized automation. By replacing rigid task evaluation mechanisms with an adaptive, context-aware approach, this work enhances adaptive coordination in multi-agent systems, providing a scalable and computationally efficient path toward embodied AGI in dynamic AI environments. Our findings highlight new directions for optimizing real-time task evaluation in language-based AI models, distributed systems, and AGI-driven decision support.

We propose a new theoretical framework in which gravitational attraction is not a direct consequence of mass, but a manifestation of quantum entanglement resistance. In this model, mass is reinterpreted as the density of internal quantum entanglement, and gravitational interaction emerges from the increasing energetic cost of forming external entanglement connections with systems that are already internally saturated. This paradigm offers an alternative to the classical geometric approach of General Relativity and instead treats gravity as an emergent informational phenomenon rooted in the structure of quantum correlations. We analyze how this model aligns with existing theoretical frameworks such as ER=EPR, entropic gravity, and holographic dualities, and demonstrate its originality through unique conceptual structures not found in existing literature.

The Three-Body Problem is a foundational challenge in physics and mathematics, known for its analytic intractability and chaotic sensitivity to initial conditions. This manuscript presents a rigorous, physically justified resolution by unifying classical, quantum, and informational paradigms. We demonstrate that, rather than admitting a unique deterministic solution, the three-body system must be described by a probability landscape of potential scenarios, consistent with modern quantum mechanics, chaos theory, and the Theory of Essence [1]-a conceptual framework treating reality as a field of potentials. By leveraging quantum-classical correspondence, Wigner phase-space methods, and recent advances in quantum simulation, we show that observable dynamics emerge as probabilistic slices or "collapses" of this broader landscape, with direct implications for astronomy, astrophysics, and multi-body system engineering. Experimental evidence and numerical simulations confirm that all measured outcomes align with probability distributions, not isolated trajectories. This work illustrates that the Three-Body Problem serves as a paradigm for understanding complex systems, where reality is defined by the dynamic interplay of measurement, potential, and structural topology. The findings establish a new standard for physically grounded, prediction-oriented approaches to nonlinear multi-body dynamics.

We introduce Shell as Code, a model in which command-line documentation is treated as live specification. We implement this paradigm through CLII, a runtime interpreter that transforms Unix command-line documentation into programmable, type-safe API objects, enabling live scripting of system utilities as if they were first-class functions in a domainspecific language. Unlike static shell scripts or hard-coded wrappers, CLII parses man pages at runtime and synthesizes type-safe abstractions over CLI commands. Each tool becomes a reactive object: its options exposed as typed fields (booleans, enums, paths, etc.), and its invocation modeled as a method with strict input validation and autocompletion. For example, ls becomes an object with flags like .all = true, .longFormat = true, and .paths = [...], while tar can be constructed dynamically with .create, .gzip, .file, and .paths fields. These structures are not handwritten but generated on the fly from live system documentation. CLII introduces three key technical advances: (1) runtime semantic extraction of CLI syntax and typing, (2) zero-effort integration with legacy tools, and (3) compositional APIs that turn CLI scripting into structured programming. The result is a shell environment that behaves more like an interpreter of a live DSL-one where commands are discoverable, safe, and composable, and where automation becomes robust by design. This framework lays the groundwork for future systems where documentation, validation, and execution are unified through a shared semantic layer.

Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.

The Theory of the Essence presents a revolutionary framework that redefines the fundamental nature of reality, time, and existence. It introduces Essence as the core structure unifying all aspects of reality – past, present, and future – addressing not only how the universe functions but also why it exists in its current form. Essence serves as the ultimate foundation, guiding the unfolding of reality through an intrinsic striving toward the search for truth. This theory builds upon and is confirmed by foundational scientific principles such as Einstein’s theory of relativity, quantum mechanics, and cosmology. By expanding these frameworks, the Theory of the Essence integrates both physical and metaphysical dimensions, providing new insights into time, causality, and the nature of potentiality while reinforcing the validity of established scientific laws. With profound implications for both scientific and philosophical inquiry, this theory opens new avenues for research, inviting exploration into the deeper truths of existence and the forces that shape reality.

This manuscript introduces Conscious Anarchy, a philosophy designed to align human society with principles of sustainability, equity, and cooperation. It addresses the flaws of current systems – control, inequality, and unsustainable hierarchies – by proposing a transformative framework grounded in decentralized governance, ethical resource distribution, and the responsible use of technology. A foundational ethical pillar within this framework is the concept of civil integrity — defined as the capacity of individuals to act freely without compromising the structural stability of society, even in the absence of external control. Civil integrity represents an internalized measure of responsibility that enables systemic balance to be maintained from within. It is not based on subjective moral belief, but on the objective criterion of non-destructive alignment with collective structure. As such, it is the essential quality of the individual within Conscious Anarchy, enabling social cohesion to emerge without coercion. Conscious Anarchy fosters a gradual awakening to humanity’s interconnectedness. It emphasizes practical steps such as integrating empathy into education, testing decentralized governance models, and leveraging technology for transparent resource management — all of which require the operational presence of civil integrity as a stabilizing force. While full transformation may take generations, immediate action is essential. By embracing balance, cooperation, and civil integrity as a structural ethical basis, humanity can avert systemic collapse and create a sustainable, equitable future. Conscious Anarchy invites individuals, communities, and institutions to contribute to this shared vision of lasting harmony and collective growth.

Modern multi-agent systems (MAS) often rely on static task allocation methods, which are insufficient for dynamic environments requiring continuous task reassignment and optimization. This paper introduces a theoretical framework that integrates fine-tuned large language models (LLMs) into MAS to enable semantic task evaluation and dynamic task distribution. By analyzing language-based task states, the system identifies hidden dependencies and adapts task allocation in real time. While the proposed system is in the conceptual phase, it offers a promising solution for automating complex workflows, reducing human intervention, and improving scalability in dynamic environments. Future work will focus on prototyping and validating the framework in real-world scenarios.

You have always been interested what does the version of the product mean and how to manage these versions. Let’s consider these questions.

As a MSc in Technical Sciences, my educational background involves the application of psychological science principles in the context of designing everyday products, systems, and devices. Specifically, I have focused on the development of a "smart house" that addresses and resolves intricate technical challenges associated with integrating complex household electronic systems into the domain of software engineering. The primary objectives of this endeavor have been to ensure the safety and operational efficiency of these integrated electronic systems, ultimately leading to a high level of control and performance within the smart house environment. This educational journey has equipped me with the knowledge and expertise necessary to tackle the technical intricacies of merging psychology and engineering to create smart, efficient, and safe living spaces.

I specialised in a branch of electrical engineering focused on electromagnetic radiation, including areas such as radio waves, broadcasting, navigation, and television. The goal was to develop proficiency in managing radioelectronic software using existing tools. Consequently, I graduated with a comprehensive understanding of radio engineering schematics and the capability to control them through software within a processor.