arXiv:2605.22240v2 Announce Type: replace Abstract: Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance within a bounded number of turns. Yet post-trained LLMs are inherently conservative, and reward-shaping RL (e.g., GRPO) struggles since it only re-weights what an already passive policy samples. We show that conditioning on the user's latent concerns unlocks proactive capability that no amount of sampling can undermine, establishing these concerns as a pivotal trainin
The increasing sophistication of LLMs and the demand for more autonomous, effective AI applications in customer interaction are driving research into proactivity.
This research outlines a method to significantly enhance the proactivity of AI in task-oriented dialogues, moving beyond reactive systems to inherently persuasive and goal-driven agents.
The ability of AI agents to proactively steer conversations and achieve goals will be significantly improved, making them more effective in complex, user-facing applications.
- · AI Agent developers
- · Customer service sectors
- · Sales and marketing automation
- · Businesses deploying AI for outbound interactions
AI agents become more efficient and persuasive in achieving defined objectives in conversational settings.
Increased adoption of proactive AI agents across industries, leading to greater automation of complex dialogues and potentially higher conversion rates.
Societal shifts in human-AI interaction dynamics, with AI actively guiding and influencing user decisions in new ways.
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Read at arXiv cs.AI