Jev is a"System One"model that returns a choice among given options instead of generating text. We study how such a specialized decision model compares with general-purpose large language models (LLMs). We evaluate Jev on 13 multiple-choice benchmarks covering knowledge, reasoning, and multilingual understanding, and c...
Xing Li, Qing-Cheng Chang, Jin-Zhong Ning et al.· 0 citations
Evaluated Jev on two stance detection datasets, VAST and ZS-CSD, shows competitive performance on VAST, matching GPT-5.6 and outperforming the other general-purpose LLMs, but falls behind stronger LLMs on ZS-CSD, particularly in distinguishing favor from against.
Xing Li, Jin-Zhong Ning, Yi-Jia Zhang et al.· 0 citations
Autonomous agents powered by large language models (LLMs) continuously accumulate experience through interaction, creating an opportunity to improve future behavior through self-evolution. A fundamental challenge is how to transform abundant, task-specific interaction experience into reusable model competence without s...
Wen-Bo Gao, Zhao-Mou Song, Zhi-Yuan Ji et al.· 1 citation
The studied model realizes only part of the flexibility afforded by four-stream mHC: individual blocks rarely require all four streams, and late residual mixing provides little measured benefit.
Peng-Xiang Zhao, Xing Li, Xian-Zhi Yu et al.· 1 citation
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