---
id: 20260724-T0-08
title: "PEARL：用自然语言做优化建模，求解器在训练中当老师"
title_en: "PEARL: Interactive Optimization Modeling from Natural Language with Solver Feedback"
url: https://ai.daily.yangsir.net/daily/20260724-T0-08
issue_date: 2026-07-24
publish_date: 2026-07-23T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2607.18256
---

# PEARL：用自然语言做优化建模，求解器在训练中当老师

arXiv 新论文提出 PEARL 框架，让 LLM 从自然语言描述中自动生成数学优化模型和可执行求解器代码，并引入求解器在环（solver-in-the-loop）的交互式反馈机制持续改进。相比一次性生成，PEARL 能根据求解器的执行结果修正模型错误，生成更可靠的优化程序。对运筹、物流、排班等领域的从业者，可大幅降低建模门槛。

## English Version

**PEARL: Interactive Optimization Modeling from Natural Language with Solver Feedback**

A new arXiv paper presents PEARL, an interactive framework that turns natural language descriptions into formal optimization models and executable solver code. It uses a solver-in-the-loop feedback mechanism to iteratively correct modeling errors. Compared to one-shot generation, PEARL produces more reliable optimization programs, lowering the barrier for practitioners in logistics, scheduling, and operations research.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2607.18256)

**详情页**：https://ai.daily.yangsir.net/daily/20260724-T0-08

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