PACE (Pre-execution Admission via Code Embeddings), an online-adaptive admission control framework that improves budgeted sample efficiency by estimating candidate utility prior to execution from within-run execution history, without training a separate offline predictor.
Gangyi Zhao, He-Bin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
Large Language Model (LLM)-driven AutoML agents have shown strong capabilities in constructing end-to-end machine learning pipelines. However, their effectiveness is limited by costly execution-based feedback, which can make the search for high-quality solutions inefficient under restricted computational budgets. We pr...
Gangyi Zhao, Hebin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
EvoThink is proposed, a framework that reduces redundant verification and encourages the exploration of new reasoning paths that not only substantially reduces inference-time token usage but also improves the reasoning capability of LRMs.
Xinbang Dai, Zheyu Xin, Hui-Kang Hu et al.· arXiv.org· 0 citations
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