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cs.CL2026

Does the Same Token Mean the Same State? MoE Routing as Signal for Reasoning Control

Kang Chen, Minshen Yu, Junjie Nian +3

In sparse Mixture-of-Experts language models, does the same token id imply the same router state and the same experts producing it? Holding the emitted token id fixed at repeated a…

cs.CL2026

Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models

Boyi Deng, Xu Wang, Yaoning Wang +15

Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, limiting our ability to inspec…

cs.CL2025

Do LLMs Signal When They're Right? Evidence from Neuron Agreement

Kang Chen, Yaoning Wang, Kai Xiong +4

Large language models (LLMs) commonly boost reasoning via sample-evaluate-ensemble decoders, achieving label free gains without ground truth. However, prevailing strategies score c…

cs.CL2025

EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization

Yaoning Wang, Jiahao Ying, Yixin Cao +2

The rapid advancement of large language models (LLMs) and the development of increasingly large and diverse evaluation benchmarks have introduced substantial computational challeng…

cs.CL2025

Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric

Yixin Cao, Jiahao Ying, Yaoning Wang +3

Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid deve…