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English(EN) NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

NEMO系统使用自主代理进行优化建模

研究人员开发了NEMO,一个使用自主编码代理将决策问题的自然语言描述转化为可执行数学优化模型的系统。与以往经常产生无效代码的方法不同,NEMO的代理在一个沙盒环境中运行,确保代码的可执行性,并实现自动化验证和修复。该系统采用了新颖的协调模式,包括非对称验证循环和用于经验重用的外部内存,在九个优化基准测试中取得了最先进的性能。 AI

影响 自主代理现在可以将自然语言转化为可执行的优化模型,有可能简化复杂的解决问题过程。

排序理由 该集群包含一篇详细介绍新系统及其在基准测试中性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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NEMO系统使用自主代理进行优化建模

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该集群包含一篇详细介绍新系统及其在基准测试中性能的学术论文。[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) · Yang Song, Anoushka Vyas, Zirui Wei, Sina Khoshfetrat Pakazad, Henrik Ohlsson, Graham Neubig ·

    NEMO:通过自主编码代理实现执行感知优化建模

    arXiv:2601.21372v2 Announce Type: replace Abstract: We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous coding agents (ACAs). Existing approaches rely on spe…