让对话又快又准：异步知识调度让响应提速95%仍保高质量
Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
- 用三模块异步调度：实时评估知识够不够，快速回复，后台悄悄补知识
- 在TopiOCQA上响应时间从23.38秒缩至1.09秒，降了95.3%
- 适合要低延迟又不失准确的开放域对话系统开发者
开放域对话AI中的延迟与质量难以兼顾：全面获取知识会导致响应过慢。现有方法要么轻量模型响应快但推理浅，要么工具增强的ReAct代理虽事实准确却因同步执行阻塞交互。本文提出PMFR，通过时间解耦框架实现异步知识编排，从根本上解决矛盾。该框架包含三个协同组件：(1) 知识充分性评估器，实时判断知识是否足够；(2) 轻量级响应生成器，立即回应用户；(3) 异步知识精炼代理，在后台补充知识。此架构保持连续对话流，通过智能触发机制逐步提升知识覆盖。在TopiOCQA上的评估显示，PMFR相比暴力扩容显著提升效率：响应时间由23.38秒降至1.09秒，降低95.3%，同时保持与重型同步基线相当的质量（GEval-C: 0.613 vs. 0.620）。
原文摘要 · Abstract (English)
The latency-quality tradeoff is a fundamental constraint in open-domain dialogue AI systems, since comprehensive knowledge access necessitates prohibitive response delays. Contemporary approaches offer two inadequate solutions: lightweight instruct models achieve sub-second latency but lack reasoning depth, while tool-augmented ReAct agents enhance factuality through external knowledge at the cost of synchronous execution that blocks interaction during retrieval processes. PMFR is thus proposed, with a temporal decoupling framework that fundamentally resolves the contradiction through asynchronous knowledge orchestration. PMFR employs three coordinated components: (1) a Knowledge Adequacy Evaluator for real-time sufficiency assessment, (2) a Lightweight Response Generator for immediate user interaction, and (3) an Asynchronous Knowledge Refinement Agent for background knowledge enhancement. This architecture maintains continuous conversational flow while progressively enriching knowledge coverage through intelligent triggering mechanisms. Evaluation results on TopiOCQA demonstrate PMFR outperforms brute-force scaling: PMFR achieves 95.3% latency reduction (23.38s -> 1.09s) while preserving response quality comparable to heavyweight synchronous baselines (GEval-C: 0.613 vs. 0.620).
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