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English(EN) MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning

新的MCCE框架结合LLM实现高级离散优化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为MCCE（多LLM协作协同进化）的新框架，旨在解决复杂的、多目标的离散优化问题，例如分子设计。该框架结合了一个固定的、强大的闭源LLM和一个较小的、可训练的模型。该系统从过去的搜索过程中学习，并通过强化学习对可训练模型进行优化，从而使两个模型都能增强彼此的能力。在药物设计基准上的实验表明，MCCE通过有效地融合知识驱动的探索和经验驱动的学习，取得了最先进的结果，优于现有方法。 AI

影响 能够实现混合LLM系统的持续进化，可能推动药物发现和其他复杂优化任务的进步。

排序理由 该集群包含一篇详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MCCE框架结合LLM实现高级离散优化

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该集群包含一篇详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nian Ran, Zhongzheng Li, Yue Wang, Qingsong Ran, Xiaoyuan Zhang, Shikun Feng, Richard Allmendinger, Xiaoguang Zhao ·

    MCCE：离散空间中基于相似度过滤偏好学习的多LLM协作搜索框架

    arXiv:2510.06270v2 Announce Type: replace-cross Abstract: Multi-objective discrete optimization problems, such as molecular design, pose significant challenges due to their vast and unstructured combinatorial spaces. Traditional evolutionary algorithms often get trapped in local …

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