arXiv:2607. 01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed.
By Yufan Lu, Xinhui Liu, Chenyang Xu, Yuxi Zhou, Hao Wang, Shenda Hong
arXiv:2608. 08853v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) routers commonly use the same scores both to select experts and to weight their already-computed outputs.
By Zongfei Li
ACE introduces a training‑free, calibration‑free framework for adaptive expert skipping in Mixture‑of‑Experts LLMs. It combines a Global Spectral Proxy that estimates global transformation capacity with a Router‑Conditioned Refinement that builds expert‑specific direction prototypes, enabling the model to skip low‑contribution experts while always keeping the top‑1 expert. Offline computation of expert statistics leaves only lightweight table lookups during inference, and experiments on three MoE‑based LLMs show ACE outperforms static and dynamic baselines, especially at high skipping ratios.
By Zukang Xu, Zhixiong Zhao, Xing Hu, Jiangyong Yu, Houji Wen, Jun Li, Zhe Jiang, Dawei Yang
arXiv:2609.36222v1 Announce Type: new
Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferrin...
By Ali Abbasi, Justin Shi, Soheil Kolouri
The paper investigates whether a sparse dictionary can reuse the routing decisions already made by a mixture‑of‑experts (MoE) model. Using the NR‑SAE approach, each routed expert is given a small dictionary that is opened only when the frozen router selects that expert. Experiments on Gemma 4 26B‑A4B show that the expert‑aligned autoencoder explains 51.8 % of the variance in router‑weighted expert outputs, outperforming several control variants and suggesting that the router already encodes useful information that a dictionary would otherwise need to learn.
By Andrew Bistras, Marty Johnson
The paper introduces GRIP, an algorithm‑agnostic framework for machine unlearning in Mixture‑of‑Experts large language models. GRIP enforces hard geometric constraints on router updates, projecting gradient changes into the null space of the retain set’s routing matrix to prevent routing manipulation. Two variants—training‑time stochastic projection and post‑training analytical correction—show significant improvements in routing stability, retain accuracy, and resistance to white‑box adversarial recovery across two MoE models.
By Andy Zhu, Rongzhe Wei, Yupu Gu, Pan Li