Author

Yun-Qin Zhu

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#artificial intelligence Preprint Oct 2026

Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation

LLM-based generative recommendation reads long, text-rich interaction histories but generates only short semantic identifiers (SIDs). This asymmetry makes supervised fine-tuning inefficient: encoding the history is expensive, yet each history usually provides supervision for only one target. We present Merge and Branch...

Yun-Qin Zhu, Li-Yang Shu, Yu-Si Zhang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation

OutageDiT is introduced, a foundation model for generating seven-day outage trajectories at quarter-hour resolution, trained on outage and weather records across the United States, which improves forecast accuracy and scenario quality over strong baselines and supports zero-shot transfer to held-out regions.

Yun-Qin Zhu, Feng Qiu, Yao Xie · 0 citations