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Renmin University of China, Institute of Automation, Chinese Academy of Sciences 2 School of Artificial Intelligence, University of Chinese Academy of Sciences 3 Wuhan AI Research 4 GWM AI Lab wangchen2020@ia.ac.cn jjzhang@nlpr.ia.ac.cn Corresponding author
Tsinghua AI3
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An 80B model that runs like a 3B? Qwen3-Coder-Next shows you can get competitive coding agent performance with a fraction of the active parameters, thanks to smart training.
LLMs can now capture an author's unique voice in translations, thanks to a multi-agent system guided by a "Stylistic Feature Spectrum" derived from wavelet transforms.
LLMs can solve competitive coding problems much more reliably by actively searching for the *right* test cases, rather than relying on random or pre-defined inputs.
