Author

Mingkui Tan

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Preprint Aug 2026

Preference-Driven Online Adaptation for Personalized Interaction Initiation in Proactive AI Assistants

Evidence-driven Online Preference Adaptation (EOPA), which grounds a user's interaction-timing preferences in measurable contextual evidence through two evidence carriers: temporal preference anchors and evidence-bearing activity prototypes, is proposed.

Yufeng Wang, Wei Zhang, Zhi-Quan Wen et al. · 1 citation

#machine learning Review Sep 2026

A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference

The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation...

Shuai-Cheng Niu, Guo-Hao Chen, Yaofo Chen et al. · 2 citations