Chinese large language models (CLLMs) are rapidly transitioning from research to deployed infrastructure across multiple sectors. Yet current evaluation practice remains fragmented and benchmark-centric, conflating language quality with general capability and weakening comparability across models. This is especially cr...
Aijia Zhong, Lei Li· Journal of ICT Standardizati...· 0 citations
AutoResearchEval, featuring 100 tasks grounded in published frontier science across 7 scientific domains and the full research lifecycle, and ARFT, a framework of 45 empirically-grounded failure patterns to enable scalable fine-grained attribution, are introduced.
Reconstruction is introduced, a blind idea-recovery benchmark that withholds the seed paper and all contemporaneous or future literature, and asks models to propose hypotheses that an independent large language model judge matches against the held-out ground-truth idea.
Shao-Long Chen, Yanlin Fei, Nazhou Liu et al.· 0 citations
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