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.

This paper introduces an innovative approach to task completion analysis in multi-agent systems using large language models (LLMs) [3]. Unlike traditional methods, which rely on binary indicators of task completion, our approach emphasizes iterative, context-driven task distribution and execution [7]. Tasks are dynamically redistributed by the Centralized Control Unit (CCU), which operates as a coordinator rather than a final decision-maker on task completion. The thematic context of the first incoming task serves as a guiding framework, allowing agents to iteratively refine their operations and share progress through language-based outputs [1]. The system leverages LLM-generated insights to assess task states, recommend further actions, and dynamically adapt task assignments. Task completion is not determined by static metrics but through linguistic confirmations and iterative feedback loops. This adaptive framework ensures unresolved tasks continue to circulate until a comprehensive resolution is achieved, enabling seamless management of evolving and interdependent tasks in multi-agent environments. The proposed approach offers significant advantages in dynamic environments by fostering adaptability, mitigating bottlenecks, and providing a scalable solution for managing complex, context-dependent tasks across various domains.

Modern multi-agent systems (MAS) often rely on static task allocation methods, which are insufficient for dynamic environments requiring continuous task reassignment and optimization [1]. 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 [2]. By analyzing language-based task states, the system identifies hidden dependencies and adapts task allocation in real time [3]. 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 [4].