REVIEW 14 cited by
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
read the original abstract
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.
Forward citations
Cited by 14 Pith papers
-
ProactBench: Beyond What The User Asked For
ProactBench measures LLM conversational proactivity in three phases using 198 multi-agent dialogues and finds recovery behavior hard to predict from existing benchmarks.
-
An Empirical Study of Proactive Coding Assistants in Real-World Software Development
Real developer IDE traces differ substantially from LLM simulations in behavior and structure; current proactive assistants are unreliable on real traces, and simulated data cannot substitute for real data in training.
-
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
METRO induces both short-term actions and long-term planning from expert transcripts into a Strategy Forest, outperforming prior methods by 9-10% on two non-collaborative dialogue benchmarks.
-
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
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...
-
CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants
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.
-
See, Infer, Intervene: Proactive World Modeling for Goal-Oriented Social Intelligence
Introduces SII framework and PIWM using AIDA and BDI models to predict intent transitions and select from five intervention classes, reporting 0.641 macro F1 with ground-truth state on a new benchmark.
-
MemCog: From Memory-as-Tool to Memory-as-Cognition in Conversational Agents
MemCog introduces a Memory-as-Cognition paradigm with Navigable Memory Store, Cross-Dimensional Navigation Interface, and Proactive Reasoning Protocol, claiming SOTA results on LoCoMo, LongMemEval, and a new Proactive...
-
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents
ProAct uses idle compute to anticipate user needs via dialogue history and memory, achieving 14.8% fewer turns, 11.7% less user effort, and 28.1% fewer hallucinations than reactive baselines on the new ProActEval benchmark.
-
OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations
OnePred predicts a user's next query in multi-turn chats from a bounded recursive intent memory, beating full-history baselines at up to 22x lower input-token cost.
-
OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations
OnePred maintains a recursively updated intent memory and uses two-stage RL to predict next queries, cutting token use by up to 22x while outperforming baselines on a new NQP-Bench dataset.
-
Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement
Vigil deploys a proactive agent for full on-call lifecycle support with autonomous self-improvement from human-resolved cases.
-
Proactive Dialogue Model with Intent Prediction
A Temporal Bayesian Network derived from MultiWOZ intent annotations predicts user intent transitions and guides proactive dialogue generation, raising Coverage AUC from 0.742 to 0.856 while cutting turns to 75% cover...
-
DiscussLLM: Teaching Large Language Models When to Speak
DiscussLLM introduces a two-stage synthetic data pipeline to annotate multi-turn discussions with five intervention types and trains LLMs to time contributions via a silent token or proactive responses.
-
Context: Proactive Goal-Directed Intelligence via Composable Sandboxed Programs, Declarative Wiring, and Structured Interaction
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.