用自主编程代理自动将自然语言转为可执行优化代码，提升准确率与可靠性。
NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
- 通过自主编程代理实现自然语言到可执行优化代码的转化
- 在9个基准上多数任务达到顶尖性能，部分数据集提升显著
- 支持代码自动验证修复，适合需要高可靠建模的工程场景
我们提出NEMO系统，利用自主编程代理（ACAs）将决策问题的自然语言描述转化为形式化的可执行数学优化实现。现有方法依赖专用大模型或特定任务代理，常生成语法错误或不可执行代码。NEMO将ACAs视为第一类抽象，类似调用API，其沙箱执行确保代码天生可执行，并支持自动化验证与修复。引入新型协作模式，包括独立生成的优化器与仿真器间的非对称验证循环、外部记忆实现经验复用，以及通过最小贝叶斯风险（MBR）解码和自一致性增强鲁棒性。在9个成熟优化基准上，NEMO在多数任务中取得领先表现，多个数据集有显著提升，证明了执行感知型智能体架构在自动化优化建模中的强大能力。
原文摘要 · Abstract (English)
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 specialized large language models (LLMs) or bespoke task-specific agents that are often brittle and frequently generate syntactically invalid or non-executable code. NEMO instead treats ACAs as a first-class abstraction analogous to API-based interaction with LLMs; their sandboxed execution guarantees code is executable by construction and supports automated validation and repair. We introduce novel coordination patterns including asymmetric validation loops between independently generated optimizer and simulator implementations, external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. Across nine established optimization benchmarks, NEMO achieves state-of-the-art performance on the majority of tasks with substantial margins on several datasets, demonstrating the power of execution-aware agentic architectures for automated optimization modeling.
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