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Ming Xu

2 papers indexed

arxivcs.LGcs.AIcs.CL2026-07-14

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

Qingyu Zhang, Qianhao Yuan, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, et al.

Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires. Two observations trace this gap. Firs…

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arxivcs.CVcs.LG2026-06-26

Scalable and Differentiable Point-Cloud Registration Using Maximum Mean Discrepancy

Rixon Crane, Fahira Afzal Maken, Nicholas Lawrance, Stanislav Funiak, Kasra Khosoussi, Ming Xu, et al.

We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points. We model registration as a nonlinear least-squares problem based on the Maximum Mean Discrepancy, approxima…

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