Author

Xia Hu

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#machine learning Preprint Sep 2026

RewardExplainer: Learning Reward Model Explanations from Counterfactual Preference Feedback

This work proposes RewardExplainer, a framework that obtains feedback from the target reward model through counterfactual rewriting and uses this feedback to further optimize the explainer, enabling the explainer to more faithfully capture the target RM's scoring preferences and sensitive behaviors than single-pass gen...

Jing-Yi He, Ni-Er Wu, Shuang Liu et al. · 0 citations

#artificial intelligence May 2026

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate

Comprehensive empirical evaluations demonstrate PEAR significantly improves average accuracy over the strongest debate baselines, and theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization.

Yang Feng, Ziwei Xu, Xia Hu et al. · arXiv.org · 0 citations