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Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
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Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper, we tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose a novel data-driven approach for this problem. Firstly, we collect real-world human activities to generate proactive task predictions. These predictions are then labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents. Building on this, we develop a comprehensive data generation pipeline to create a diverse dataset, ProactiveBench, containing 6,790 events. Finally, we demonstrate that fine-tuning models with the proposed ProactiveBench can significantly elicit the proactiveness of LLM agents. Experimental results show that our fine-tuned model achieves an F1-Score of 66.47% in proactively offering assistance, outperforming all open-source and close-source models. These results highlight the potential of our method in creating more proactive and effective agent systems, paving the way for future advancements in human-agent collaboration.
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Cited by 7 Pith papers
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After Talking with 1,000 Personas: Learning Preference-Aligned Proactive Assistants From Large-Scale Persona Interactions
A two-stage framework — category-structured fine-tuning on LLM-simulated personas plus on-device activation steering — improves proactive-assistant timing and perceived quality, though the biggest gains are measured w...
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ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
A unified evaluation framework for proactive dialogue agents, built with 328 synthetic environments across six domains, shows that thinking modes improve target planning but not dialogue guidance in a 22-model comparison.
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Galaxy: A Cognition-Centered Framework for Proactive, Privacy-Preserving, and Self-Evolving LLM Agents
Galaxy couples a cognitive tree structure with a meta-agent to make LLM assistants proactive, privacy-preserving, and self-evolving.
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Teaching Language Models To Gather Information Proactively
Rewarding questions for eliciting genuinely new information trains a small model to outperform larger models at proactive clarification and downstream writing quality.
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ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent
An LLM-based UI agent monitors visual analytics interactions, detects when users struggle, infers their intent, and proactively provides context-aware guidance.
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