提出新型框架，让智能体在不确定性中自适应调节状态。
IRAM-Omega-Q: A Computational Framework for Uncertainty Regulation in Adaptive Agents
- 用类量子态表示与闭环熵控结合，动态调节内部状态。
- 先调控优于先扰动，但长期适应增益更低。
- 揭示控制顺序影响调节阈值，适合研究鲁棒性架构的学者。
在随机扰动下，自适应智能体不仅需优化任务输出，还需维持内部状态的可操作性。本文提出IRAM-Omega-Q计算框架，用于建模此类环境中的不确定性调节机制。该框架结合类量子态表示与闭环内熵信号自适应控制：演化状态为归一化复振幅向量，其相干演化精确遵循ψ(t + Δt) = exp(−iHΔt)ψ(t)，并基于推导出的密度矩阵支持熵与相干性缺口分析。比较两种因果控制顺序：调节优先（RF）在当前周期扰动前即可启动调节，抑制输入暴露；扰动优先（DF）则在扰动后才生成新调节响应，实现反应式稳定。匹配种子的发布模式仿真显示，两者相干性缺口轨迹总体相似，但RF下持续适应增益较低；基于燃烧期后时间波动的敏感性地图进一步表明，DF将临界初始增益脊移向更大值，跨越多个扰动区间。结果表明，控制顺序是调节需求与阈值位置的架构决定因素，即便在共享的系统结构中亦然。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。