IRAM-Omega-Q: A Computational Architecture for Uncertainty Regulation in Artificial Agents
arXiv:2603.16020v1 Announce Type: new Abstract: Artificial agents can achieve strong task performance while remaining opaque with respect to internal regulation, uncertainty management, and stability under stochastic perturbation. We present IRAM-Omega-Q, a computational architecture that models internal regulation as closed-loop control over a quantum-like state representation. The framework uses density matrices instrumentally as abstract state descriptors, enabling direct computation of entropy, purity, and coherence-related metrics without invoking physical quantum processes. A central adaptive gain is updated continuously to maintain a target uncertainty regime under noise. Using systematic parameter sweeps, fixed-seed publication-mode simulations, and susceptibility-based phase-diagram analysis, we identify reproducible critical boundaries in regulation-noise space. We further show that alternative control update orderings, interpreted as perception-first and action-first archit
arXiv:2603.16020v1 Announce Type: new Abstract: Artificial agents can achieve strong task performance while remaining opaque with respect to internal regulation, uncertainty management, and stability under stochastic perturbation. We present IRAM-Omega-Q, a computational architecture that models internal regulation as closed-loop control over a quantum-like state representation. The framework uses density matrices instrumentally as abstract state descriptors, enabling direct computation of entropy, purity, and coherence-related metrics without invoking physical quantum processes. A central adaptive gain is updated continuously to maintain a target uncertainty regime under noise. Using systematic parameter sweeps, fixed-seed publication-mode simulations, and susceptibility-based phase-diagram analysis, we identify reproducible critical boundaries in regulation-noise space. We further show that alternative control update orderings, interpreted as perception-first and action-first architectures, induce distinct stability regimes under identical external conditions. These results support uncertainty regulation as a concrete architectural principle for artificial agents and provide a formal setting for studying stability, control, and order effects in cognitively inspired AI systems. The framework is presented as a technical model of adaptive regulation dynamics in artificial agents. It makes no claims regarding phenomenological consciousness, and the quantum-like formalism is used strictly as a mathematical representation for structured uncertainty and state evolution.
Executive Summary
The article presents IRAM-Omega-Q, a computational architecture for uncertainty regulation in artificial agents. The framework models internal regulation as closed-loop control over a quantum-like state representation and uses density matrices to enable direct computation of entropy, purity, and coherence-related metrics. Through systematic parameter sweeps and susceptibility-based phase-diagram analysis, the authors identify reproducible critical boundaries in regulation-noise space and demonstrate distinct stability regimes under alternative control update orderings. This work supports uncertainty regulation as a concrete architectural principle for artificial agents and provides a formal setting for studying stability, control, and order effects in cognitively inspired AI systems.
Key Points
- ▸ IRAM-Omega-Q is a computational architecture for uncertainty regulation in artificial agents
- ▸ The framework models internal regulation as closed-loop control over a quantum-like state representation
- ▸ Density matrices are used to enable direct computation of entropy, purity, and coherence-related metrics
Merits
Strength in Mathematical Representation
The use of a quantum-like formalism provides a structured and mathematically rigorous representation of uncertainty and state evolution, which is a significant strength of the framework.
Insight into Stability and Control
The framework's ability to identify reproducible critical boundaries in regulation-noise space and demonstrate distinct stability regimes under alternative control update orderings provides valuable insights into the stability and control of artificial agents.
Formal Setting for Studying Cognitive AI Systems
The proposed framework provides a formal setting for studying stability, control, and order effects in cognitively inspired AI systems, which is a significant contribution to the field of artificial intelligence.
Demerits
Limitation in Practical Application
The framework's reliance on a quantum-like formalism may limit its practical application in real-world scenarios, where physical quantum processes are not relevant.
Need for Further Experimental Validation
The framework's performance and stability in real-world scenarios require further experimental validation, which may be challenging to achieve.
Expert Commentary
The proposed framework, IRAM-Omega-Q, is a significant contribution to the field of artificial intelligence, particularly in the area of cognitive architectures and uncertainty management. The use of a quantum-like formalism provides a mathematically rigorous representation of uncertainty and state evolution, which is a key strength of the framework. However, the framework's reliance on this formalism may limit its practical application in real-world scenarios. Furthermore, the framework's performance and stability in real-world scenarios require further experimental validation. Despite these limitations, the proposed framework has the potential to improve the stability and control of artificial agents in complex and dynamic environments, which is a critical area of research in artificial intelligence.
Recommendations
- ✓ Further experimental validation of the framework's performance and stability in real-world scenarios is necessary to fully evaluate its potential.
- ✓ The framework's focus on uncertainty regulation may have implications for the development of more robust and reliable AI systems, which could inform policy decisions related to the deployment of AI in critical applications.