arXiv AI
Jul 14
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?
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.
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arXiv AI
Sep 4
Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation
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.
Hugging Face Trending Papers
Jul 13
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
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 AI
Jun 15
StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance
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.
Hugging Face Trending Papers
Jul 4
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 2
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.
arXiv AI
Jul 7
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
arXiv:2607. 03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks.
arXiv AI
Aug 18
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
arXiv:2608. 15755v1 Announce Type: new Abstract: User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints.
arXiv AI
Jul 10
Context Graphs for Proactive Enterprise Agents
arXiv:2607. 07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting.
arXiv AI
Jun 6
SentinelBench: A Benchmark for Long-Running Monitoring Agents
arXiv:2606. 05342v1 Announce Type: new Abstract: AI agents are increasingly asked to carry out work that spans minutes, hours, or longer.
arXiv AI
Aug 7
Unified Agent: Managing Interactions across Devices
arXiv:2608. 05729v1 Announce Type: new Abstract: As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time.
arXiv AI
Jun 10
$\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
arXiv:2606. 10156v1 Announce Type: cross Abstract: As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace.