Line of inquiry
Inquiring lines›What makes reasoning better — more…›What limits conversational AI effe…›this line of inquiry
How should conversational agents balance goal-driven initiative with user control?
A broader line of inquiry — a family of 59 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 59
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- How do conversational agents overcome structural passivity and goal awareness gaps?
- Can conversation analysis predict when agents should ask users for clarification?
- Does proactive agent design improve conversation efficiency or create user frustration?
- Why do conversational agents lack the goal awareness needed to lead rather than just respond?
- What social boundaries must proactive agents respect during conversation?
- How do insert-expansions help systems probe users before silently diverging?
- When should agents use clarification commands instead of assuming intent?
- Can agents balance goal-driven proactivity with user preference alignment?
- Can prompt engineering overcome the gulf between user intent and AI interpretation?
- How can agents detect whether users are willing to follow their topic guidance?
- When should agents accommodate user preferences over their own goals?
- Why do AI models treat user intent as binary rather than evolving?
- Can users articulate what they want before AI helps them discover it?
- How can dialogue structure and trajectory predict social agent performance?
- Why do agents make premature commitments when user goals are still forming?
- Why do passive conversational agents fail at collaborative decision-making?
- Do conversational agents need goal awareness to initiate grounding work themselves?
- Can AI take initiative by questioning without being proactive in directive ways?
- How can agents learn user preferences during conversation without pre-calibration?
- Can proactive AI agents deploy politeness strategies without appearing intrusive?
- How does intrinsic motivation drive conversational agents beyond passive responsiveness?
- When should an AI system actively intervene versus remain silent?
- Why are task-oriented dialogue datasets systematically underrepresenting human proactive behavior?
- Why can't current AI agents lead conversations with users?
- What makes complex UI navigation and social interaction harder than task completion?
- Why do dialogue systems fail to detect declarative clarification requests?
- Can real-time detection identify when users have incomplete or underdeveloped intent?
- How can agents learn to estimate user satisfaction in real-time during conversation?
- What multi-turn reward structures would encourage active intent discovery?
- Can topic planning and response generation reduce dialogue turns?
- How do agents decide when to abstain from contributing?
- Can curiosity-driven dialogue incrementally discover user interest journeys in real time?
- Why might text-only interfaces underestimate agent preference elicitation capabilities?
- How might dual-process dialogue use information gain to trigger clarification?
- Can safety training in chat scenarios transfer to agentic task performance?
- Can users articulate their intent before exploring what an AI system finds?
- How do users fail to articulate what they actually want?
- What dialogue dynamics distinguish negotiation from standard information-provision tasks?
- Can hierarchical reinforcement learning manage phase-dependent initiative switching in dialogue?
- Can generative interfaces help users articulate what they actually want?
- Can a separate mediator layer improve intent understanding before task execution?
- Do behavioral cues enable proactive AI without event-triggered decision points?
- Why do traditional interfaces bypass the intention formation problem that language models expose?
- What signals should systems use to predict the right moment for intervention?
- How does asymmetric information between users and agents relate to proactivity?
- What distinguishes proactive information provision from proactive clarification seeking?
- What makes LLM agents default to passive helpfulness without curiosity rewards?
- Can agents learn user intent from unlabeled video without text labels?
- What interaction history signals indicate what a participant finds relevant?
- What makes proactivity useful instead of intrusive in conversation?
- What interaction mechanisms let humans and agents defer work effectively?
- What data would be needed to train proactive conversational systems?
- What makes proactive conversational agents feel intrusive versus helpful to users?
- Why do AI agents default to passivity when deferral timing is unclear?
- How do question acts and intents map to speech act theory?
- How should systems learn what each meeting participant actually cares about?
- What speaker selection protocol prevents both stalling and premature convergence?
- What distinguishes first-order from second-order agency in language models?
- What makes idle window detection valuable for continuous agent improvement?