Training Proactive and Personalized LLM Agents
Quick summary
arXiv:2511.02208v2 Announce Type: replace Abstract: Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communicate and adapt to people. To facilitate this shift in real-world complex applications, we first formalize three dimensions of collaborative AI agents: Productivity, Proactivity, and Personalization (PPP). We introduce UserVille, an interactive environment with configurable LLM-based user simulators and user-centric feedback to evaluate these dimensions, and propose a
Key takeaways
- arXiv:2511.02208v2 Announce Type: replace Abstract: Despite rapid progress, current AI agents are primarily optimized for isolated task completion.
- We argue for a paradigm shift toward training agents as collaborators that communicate and adapt to people.
- To facilitate this shift in real-world complex applications, we first formalize three dimensions of collaborative AI agents: Productivity, Proactivity, and Personalization (PPP).
Why it matters
“Training Proactive and Personalized LLM Agents” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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