arXiv AI
Jarvis: A Proactive Speech Agent for Multi-Party Conversations
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ProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would benefit from timely intervention as human collaborators often do. This reactive design substantially limits the use of agents as active participants in multi-user collaboration, where disagreements, ambiguous goals, forgotten constraints, underspecified plans, discussion loops, and imbalanced participation can gradually undermine group progress.
arXiv AI
Jun 12
Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
arXiv:2606. 13544v1 Announce Type: cross Abstract: Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations.
Hugging Face Trending Papers
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Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings.
arXiv Computation and Language
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MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue
arXiv:2610.20172v1 Announce Type: cross Abstract: Full-duplex speech systems must decide when to enter, hold, or relinquish the conversational floor, even as multiple speakers overlap or backchannel...
arXiv AI
Jul 21
ProEvent: An Event-centric Benchmark for Proactive Agents
arXiv:2607. 17701v1 Announce Type: new Abstract: Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions.
arXiv AI
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Just A Rather Very Intelligent Spoken Agent
arXiv:2607. 16610v1 Announce Type: new Abstract: Long-horizon AI agents are becoming increasingly capable, yet their interaction with users remains surprisingly thin.