Author

Xin-Yi Luo

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Preprint Aug 2026

Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models

Mixture-of-Experts (MoE) models decouple parameter count from per-token compute, but deployment still requires hosting every expert in memory. Recent theory shows that experts whose router weights change least during fine-tuning can be pruned with provable accuracy preservation, yet the guarantee assumes full fine-tuni...

Ali Janati, K. El Maghraoui, Cheng-Ke Zou et al. · 0 citations

Preprint Aug 2026

UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

Experiments show that UI-Mate-27B sets a new open-weight state of the art on general computer-use benchmarks, substantially improving long-horizon reliability, and makes three contributions to an environment-grounded training stack with in-context demonstration learning.

Zihan Ding, Longxu Dou, Qixiao Gao et al. · 1 citation