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cs.LG2026
When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning
Chenghao Qiu, Chunli Peng, Yufeng Yang +2
In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples. However, we reveal a counterintuitive phe…
cs.LG2026
From Dual Tracking to Clipping: Provably Faster Distributionally Robust Multi-Objective Optimization
Yufeng Yang, Fangning Zhuo, Ziyi Chen +2
Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most existing MOO formulations do not expl…