IRAM-Omega-Q：适应性智能体在随机干扰下的不确定性调节计算框架
IRAM-Omega-Q: A Computational Framework for Uncertainty Regulation in Adaptive Agents
AI总结：
本文提出IRAM-Omega-Q框架，结合量子态表示与闭环自适应控制，通过比较因果控制顺序，探讨不确定性调节的架构影响。
摘要：
适应性智能体在不确定环境下必须做更多 than 仅优化任务输出：它们必须在噪声、扰动和变化条件下维持一个可行的内部状态。本文提出IRAM-Omega-Q框架，用于建模在随机干扰下适应性智能体的不确定性调节。该框架结合了量子态表示与闭环自适应控制，通过比较因果控制顺序，探讨不确定性调节的架构影响。
英文摘要：
Adaptive agents operating under uncertainty must do more than optimize task outputs: they must maintain a workable internal state under noise, perturbation, and changing conditions. This paper introduces IRAM-Omega-Q, a computational framework for modeling uncertainty regulation in adaptive agents under stochastic disturbance. The framework combines a quantum-like state representation with closed-loop adaptive control over an internal entropy signal. The quantum-like formalism is used instrumentally: the evolving state is a normalized complex amplitude vector, coherent evolution is propagated exactly as psi(t + Delta t) = exp(-i H Delta t) psi(t), and a derived density matrix supports entropy and coherence-gap analysis. Two causal control orderings are compared. In regulation-first (RF) ordering, adaptive regulation is available before current-cycle disturbance and attenuates incoming exposure; in disturbance-first (DF) ordering, current-cycle disturbance is received before a new regulatory response can be computed, and stabilization acts reactively. Publication-mode, matched-seed simulations show broadly comparable coherence-gap trajectories but lower sustained adaptive gain under RF. Susceptibility maps based on post-burn-in temporal fluctuations further show that DF shifts the critical initial-gain ridge toward larger values across multiple disturbance intervals. These results identify ordering as an architectural determinant of regulatory demand and threshold location within an otherwise shared regime structure.
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