Current autonomous AI agents, driven primarily by Large Language Models (LLMs), operate in a state of cognitive weightlessness: they process information without an intrinsic sense of network topology, temporal pacing, or epistemic limits. Consequently, heuristic agentic loops (e.g., ReAct) can exhibit failure modes in interactive environments, including excessive tool use under congestion, prolonged deliberation under time decay, and brittle behavior under ambiguous evidence. In this paper, we propose the Triadic Cognitive Architecture (TCA), a unified mathematical framework that grounds machine reasoning in continuous-time physics. By synthesizing nonlinear filtering theory, Riemannian routing geometry, and optimal control, we formally define the concept of Cognitive Friction. We map the agent's deliberation process to a coupled stochastic control problem where information acquisition is path-dependent and physically constrained. Rather than relying on arbitrary heuristic stop-tokens, the TCA uses an HJB-motivated stopping boundary and instantiates a rollout-based approximation of belief-dependent value-of-information with a net-utility halting condition. Through empirical validation in a simulated Emergency Medical Diagnostic Grid (EMDG), we demonstrate that while greedy baselines over-deliberate under latency and congestion costs, the triadic policy reduces time-to-action while improving patient viability without degrading diagnostic accuracy in this environment.

翻译：当前以大语言模型（LLM）为主驱动的自主AI智能体，处于一种"认知失重"状态：信息处理过程缺乏对网络拓扑、时间节奏与认知边界的固有感知。因此，在交互式环境中，启发式智能体循环（如ReAct）可能出现多种故障模式，包括拥塞情况下的过度工具使用、时间衰减下的过度延迟决策，以及模糊证据下的脆弱行为。本文提出三元认知架构（TCA），这是一个将机器推理植根于连续时间物理学的统一数学框架。通过综合非线性滤波理论、黎曼路由几何与最优控制理论，我们正式定义了"认知摩擦"概念。我们将智能体的推理过程映射为一个耦合随机控制问题，其中信息获取具有路径依赖性与物理约束。不同于依赖任意启发式终止标记，TCA采用基于HJB方程的停止边界，并实现了依赖于信念的信息价值的 rollout 近似计算，结合净效用停止条件。通过在模拟急诊医疗诊断网格（EMDG）中的实证验证，我们证明：在延迟与拥塞成本下，贪婪基线方法会过度推敲，而三元策略在保持诊断准确率的同时，能缩短行动时间并提高患者存活率。