AlgoEvo is introduced, a unified agentic architecture that transforms automated algorithm discovery into an interactive, knowledge-accumulating process, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed the strongest existing methods through flexible skill activat...
Jun-Hao Qiu, Qing-Long Hu, Ji Cheng et al.· 0 citations
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propos...
Jun-Hao Qiu, Qing-Long Hu, Xia-Liang Tong et al.· 0 citations
This work proposes Hyper-ES, a subspace-based ES framework that avoids the weakness of ES in full-parameter search while exploiting its strength in low-dimensional optimization, and consistently outperforms GRPO-LoRA while requiring 10% fewer space-consuming gradient updates.
Yuntian Gu, Zhi Zheng, Yunpeng Ba et al.· 0 citations
These findings position ES as a distinct reasoning post-training paradigm rather than a less effective, memory-efficient alternative to GRPO, and study how hyperparameter design affects the effectiveness of ES, demonstrating that ES requires a smaller population size in a larger LLM.
Yunpeng Ba, Zhi Zheng, Yue Xie et al.· 0 citations
DyCA treats instance clustering as a co-evolving component within the search process, reusing accumulated evaluation data as feature-free signals to progressively partition instances with similar algorithmic response patterns, thereby enabling finer-grained and more adaptive guidance for specialized algorithm design.
Qinglong Hu, Qingfu Zhang, Fei Liu et al.· 0 citations
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