The speed and accuracy of an artificial teammate fundamentally alter the failure states of Human-AI integration. While high-speed AI interventions risk inducing reflexive blind compliance, delayed interventions can induce ambiguous cognitive conflict. This study investigates how the fundamental characteristics of an in-task AI assistant, Fast/Less-Accurate (FLA-AI) versus Slow/Accurate (SA-AI) impact the synergy of Collaborative Brain-Computer Interface (cBCI) teams in a Virtual Reality drone task. Seventeen operators completed continuous search tasks under high cognitive workload while their spatial covariance was mapped using a 2D Adaptive Riemannian Oracle. The results mathematically demonstrate that AI timing dictates the mechanism of team failure. Fast AI induced instant, blind compliance; human accuracy under deception collapsed to 50.2%, and pure behavioural teams (N=8) failed to scale beyond 74.1%. In contrast, Slow AI induced delayed cognitive conflict; humans hesitated (61.1% accuracy), but N=8 behavioural teams eventually recovered to 100.0%. Crucially, the Riemannian Oracle mathematically adapted to these states: it heavily restricted temporal windows (< 0.8s) to intercept fast reflexive compliance, while widening windows (> 1.2s) to capture delayed cognitive conflict. Integrating these isolated veridical signals via Hybrid Fusion successfully rescued the Fast AI team (+7.6% at N=8) and significantly accelerated the recovery of smaller Slow AI teams (+6.9% at N=4). These findings prove that cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.

翻译：人工智能队友的速度与准确性从根本上改变了人机融合的失效模式。虽然高速AI干预有诱发反射性盲从的风险，但延迟干预可能引发模糊的认知冲突。本研究探讨了任务内AI助手的两个基本特征——快速/低准确度（FLA-AI）与慢速/高准确度（SA-AI）——如何影响虚拟现实无人机任务中协作型脑机接口（cBCI）团队的协同效应。17名操作员在高认知负荷下完成连续搜索任务，同时使用二维自适应黎曼预言机映射其空间协方差。数学结果表明，AI的响应时序决定了团队失效的机制：快速AI诱发即时盲从，人类受欺骗时的准确度骤降至50.2%，纯行为团队（N=8）的协同效果始终无法突破74.1%；对比之下，慢速AI引发延迟性认知冲突，人类虽出现犹豫（准确度61.1%），但N=8的行为团队最终恢复至100.0%。关键之处在于，黎曼预言机从数学角度自适应于这些状态：为截获快速反射性盲从，它严格限制时间窗口（<0.8秒）；为捕获延迟认知冲突，则放宽窗口（>1.2秒）。通过混合融合方法整合这些孤立的真实信号，成功挽救了快速AI团队（N=8时准确度提升7.6%），并显著加速了小型慢速AI团队的恢复进程（N=4时准确度提升6.9%）。这些发现证明cBCI协同效应高度依赖信任的时间动力学特征，为设计动态门控人机系统提供了关键框架。