arXiv AI
Vela: User Behavior as Supervision Sparks LLM Intelligence
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Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
arXiv:2609.15972v1 Announce Type: cross Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the p...
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Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself
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PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
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arXiv AI
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Training Proactive and Personalized LLM Agents
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Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
arXiv:2602. 11351v2 Announce Type: replace Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world, user-centric applications.