English(EN) WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling

新的WMLLM框架使用世界模型进行自演化优化代理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员推出了一种新颖的自演化优化代理框架WMLLM，该框架采用预测-行动世界建模方法。该方法通过首先预测有希望的方向，然后生成待评估的候选对象，从而提高了复杂、高维优化问题的样本效率。WMLLM集成了代理式多轮精炼、基于种群的搜索和强化学习，以迭代地改进其世界模型和优化策略。实验，特别是在多目标分子优化方面，证明了WMLLM在有限评估次数下实现最先进性能的能力。 AI

影响该框架可以提高复杂优化任务的效率，可能加速科学发现和工程设计。

排序理由该集群描述了一篇关于新颖优化代理框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zhongzheng Li, Qingsong Ran, Shikun Feng, Nian Ran, Wenhao Li, Xiaoyuan Zhang, Yue Wang, Xiaoguang Zhao · 2026-09-03 04:00

WMLLM：通过预测-行动世界模型实现自演化优化代理

arXiv:2609.01608v1 Announce Type: cross Abstract: Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficiency because they rely on direct candidate generation…

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WMLLM：通过预测-行动世界模型实现自演化优化代理

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