让客服机器人实时追踪热点，避免因信息滞后导致服务失效。
PulseCX: Breaking the Closed-World Assumption in Real-Time CX
- 用异步代理将实时信息转为带衰减的时序知识图谱。
- 在动态环境中提升意图识别率和客户满意度，延迟低于10ms。
- 适合需要实时响应的客户服务场景，如突发舆情应对。
客户体验（CX）中的对话式AI通常受限于封闭世界假设，无法及时响应如病毒式趋势或系统故障等外部快速变化。临时性网页搜索虽能弥补这一缺陷，但常引入高延迟和上下文污染。我们提出PulseCX框架，将知识获取与使用解耦。采用结构优先范式，PulseCX通过异步代理将信号线性化为受强化-衰减动力学控制的衰减感知时序知识图谱（DA-TKG），主动管理信息生命周期。结合层次化意图门控机制，该框架消除了同步搜索瓶颈（<10ms开销），在动态环境中显著提升意图识别率（IRR）和客户满意度（s-CSAT）。
原文摘要 · Abstract (English)
Conversational AI agents in Customer Experience (CX) typically suffer from a Closed-World Constraint, ignoring high-velocity external shifts like viral trends or outages. Ad-hoc web search attempts to bridge this gap but often introduce prohibitive latency and context poisoning. We introduce PulseCX, a framework that decouples knowledge acquisition from consumption. Adopting a structure-first paradigm, PulseCX employs an asynchronous agent to linearize signals into a Decay-Aware Temporal Knowledge Graph (DA-TKG) governed by reinforcement--decay dynamics to actively manage information lifecycles. By coupling this self-evolving memory with hierarchical intent gating, PulseCX removes synchronous search bottlenecks (<10ms overhead) and drives significant gains in Intent Resolution (IRR) and Customer Satisfaction (s-CSAT) in dynamic environments.
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