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A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects

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arxiv 2305.02750 v2 pith:7VEOV354 submitted 2023-05-04 cs.CL cs.AI

Yang Deng , Wenqiang Lei , Wai Lam , Tat-Seng Chua This is my paper · ORCID
classification cs.CLcs.AI
keywords conversationaldialogueproactivesurveysystemsadvancedagentproblems
verification ladder T0 review T1 audit T2 compute T3 formal
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Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that this first survey of proactive dialogue systems can provide the community with a quick access and an overall picture to this practical problem, and stimulate more progresses on conversational AI to the next level.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

    cs.CV 2026-07 conditional novelty 6.5 of 10

    EgoMemo uses multi-scale temporal summaries, a knowledge graph, and visual archives to decide whether and when to intervene proactively on continuous egocentric video, setting baselines on the new EgoServe benchmark o...

  2. CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants

    cs.AI 2026-06 conditional novelty 6.0 of 10

    CALLBENCH is a 50k-dialogue Chinese benchmark showing current dialogue methods achieve only ~0.61-0.77 overall scores and about 10.6% safety violations on dual-goal phone-call assistant decisions.

  3. ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents

    cs.CL 2025-08 conditional novelty 6.0 of 10

    A unified evaluation framework for proactive dialogue agents, built with 328 synthetic environments across six domains, shows that thinking modes improve target planning but not dialogue guidance in a 22-model comparison.

  4. ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent

    cs.HC 2025-07 conditional novelty 6.0 of 10

    An LLM-based UI agent monitors visual analytics interactions, detects when users struggle, infers their intent, and proactively provides context-aware guidance.

  5. Context: Proactive Goal-Directed Intelligence via Composable Sandboxed Programs, Declarative Wiring, and Structured Interaction

    cs.AI 2026-04 conditional novelty 4.0 of 10

    An architecture for proactive goal-directed AI agents is presented with six formal theorems claiming Pareto improvements over reactive chatbots in multi-participant task settings.

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