arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely
arXiv:2609.06403v1 Announce Type: new Abstract: Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal
arXiv:2608.06400v1 Announce Type: new Abstract: Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging.
arXiv:2603.29206v1 Announce Type: new Abstract: Routing is widely used to scale large language models, from Mixture-of-Experts gating to multi-model/tool selection. A common
<p>Conventional mixture‑of‑experts designs hand each transformer layer its own private expert set, causing the total expert parameter count to swell linearly with depth. Recent
arXiv:2603.22317v1 Announce Type: cross Abstract: Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a
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