Author

Jun Xu

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#machine learning Preprint Oct 2026

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

This work proposes MAEM, a replay-free, capacity-controlled dynamic mixture-of-experts framework for class-incremental graph learning that mitigates recency bias, outperforms strong continual graph learning baselines, and keeps model capacity bounded under strict replay-free learning.

Jun-Lin Chen, Wen-Hao Wang, Jun Xu et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Rethinking Self-Distillation for Multi-Teacher Capability Merging

Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-model post-training. Recent trends suggest that multi-teacher on-policy distillation (MOPD) outperforms conventional off-policy methods. However, despite the higher inferen...

Roy Xie, Dan Friedman, Feng Nan et al. · 0 citations