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Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue

Jinling Gan, Churong Liang, Runnan Li

TL;DR

Open-domain dialogue systems face a fundamental latency–quality trade-off when accessing comprehensive knowledge. The paper proposes PMFR, a temporal decoupling framework that uses a three-component architecture—a Knowledge Adequacy Evaluator, a Lightweight Response Generator, and an Asynchronous Knowledge Refinement Agent—to separate fast replies from background knowledge gathering, formalized by the multi-objective loss $L(r_t) = -\alpha \cdot Q(r_t \mid q_t, H_{t-1}) + \beta \cdot L(r_t) + \gamma \cdot C(r_t)$ with $r_t = f_{fast}(q_t, H_{t-1}, K_t)$ and $K_{t+1} = \text{async}\{ f_{slow}(q_t, H_{t-1}, K_t) \}$. On TopiOCQA, PMFR achieves a 95.3% latency reduction while maintaining a GEval-C score of 0.613, nearly matching heavyweight ReAct baselines (0.620), and exhibits substantially faster mean latency (1.09 s vs 23.38 s). This demonstrates a generalizable approach to adaptive knowledge orchestration in real-time dialogue, offering robust, responsive, and knowledge-rich interactions without blocking user experience.

Abstract

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).

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue

TL;DR

Open-domain dialogue systems face a fundamental latency–quality trade-off when accessing comprehensive knowledge. The paper proposes PMFR, a temporal decoupling framework that uses a three-component architecture—a Knowledge Adequacy Evaluator, a Lightweight Response Generator, and an Asynchronous Knowledge Refinement Agent—to separate fast replies from background knowledge gathering, formalized by the multi-objective loss with and . On TopiOCQA, PMFR achieves a 95.3% latency reduction while maintaining a GEval-C score of 0.613, nearly matching heavyweight ReAct baselines (0.620), and exhibits substantially faster mean latency (1.09 s vs 23.38 s). This demonstrates a generalizable approach to adaptive knowledge orchestration in real-time dialogue, offering robust, responsive, and knowledge-rich interactions without blocking user experience.

Abstract

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).

Paper Structure

This paper contains 14 sections, 4 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Latency-Quality Tradeoffs in Dialogue Systems
  4. Temporal Dual-Process Architectures
  5. Adaptive Knowledge Orchestration
  6. Methodology
  7. Problem Formulation
  8. PREPARED MIND, FAST RESPONSE
  9. Adaptive Response with Async Refinement
  10. Experiments
  11. Experimental Setup
  12. Baseline Systems
  13. Experimental Results
  14. Conclusion

Figures (1)

  • Figure 1: Architecture of the proposed framework is shown on the left side. On the right is a real case study demonstrating how the system evaluates knowledge sufficiency, triggers asynchronous knowledge refinement, and maintains dialogue flow.