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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.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
Less Data Less Tokens: Multilingual Unification Learning for Efficient Test-Time Reasoning in LLMs
Kang Chen, Mengdi Zhang, Yixin Cao
This paper explores the challenges of test-time scaling of large language models (LLMs), regarding both the data and inference efficiency. We highlight the diversity of multi-lingu…