AI RESEARCH
IRAM-Omega-Q: A Computational Architecture for Uncertainty Regulation in Artificial Agents
arXiv CS.AI
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ArXi:2603.16020v1 Announce Type: new 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.