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.
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arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?
The paper presents a unified framework for proactive service agents, defining proactivity as an agent’s ability to infer service opportunities from incomplete signals and decide whether to remain silent, ask, assist, or act. It models this as a partially observable sequential decision process constrained by authorization and risk, integrating timing, content, and delivery into a single structured action. The authors categorize existing methods along a decision pipeline—state and need estimation, intervention gating, action construction, and feedback adaptation—and propose standardized metrics for evaluating triggering, timing, calibration, user burden, safety, and policy value across diverse interaction modalities.
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.
arXiv:2606. 14571v1 Announce Type: new Abstract: A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance.
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:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.