LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across related tasks and sustaining improvement when training is expensive. We introduce Population Evolution (PE), a collaborative, hierarchical framework that connects ongoing lo...
Bo Peng, Li-Zhu Zhang, Yu-Hang Zhou et al.· 0 citations
These results show that evolving branch objectives and proposal policies can yield complementary harnesses whose strengths a router combines without access to test outcomes.
Hao-Yu Dong, Yu-Hang Zhou, Zi-Hao Lin et al.· 0 citations
Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, advisor-only guidance, and general retrieval, and general retrieval and that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retriev...
Across several benchmarks, it is shown that a plain, fully fine-tuned RoBERTa matches or exceeds the specialized detectors those benchmarks are built around, suggesting that progress in AI-generated text detection should be measured not only by in-distribution performance, but also by robustness under distribution shif...
Zhuoer Shen, Mingyi Wang, Shaofeng Zou et al.· 0 citations
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