← 研踪 PaperTrailarXiv:2607.18256 · v2
PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
首次提交 2026-05-14 14:47
版本更新 2026-11-08 22:38
雷达观测 2026-11-10 12:16
摘要
Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code. While recent advances in large language models have shown promise in automating this process, many approaches focus on one-shot generation or rely on predefined execution-and-repair workflows, without explicitly learning a policy for tool use and revision. This stands in sharp contrast to real-world optimization modeling, which is inherently interactive and proceeds through repeated solve-debug-revise cycles. We introduce PEARL, a system for multi-turn solver-interactive optimization modeling that uses Python execution and mathematical programming solvers inside this loop. Rather than relying on a fixed repair workflow, PEARL learns when to test partial models, how to revise from solver diagnostics, and when to stop. It operates in a multi-turn tool-integrated setting where intermediate execution results, feasibility signals, and solution checks are used to improve both formulations and solver code before finalization. Across diverse optimization benchmarks, PEARL substantially improves executable objective agreement over the same-scale base model and is competitive with strong one-shot and tool-augmented baselines. Notably, PEARL-Qwen3-4B outperforms the much larger DeepSeek-V3.2-685B baseline under direct prompting on both macro- and micro-averaged Pass@1. Code and datasets are provided at https://github.com/AuroraLHL/PEARL.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-08 22:38 · 最新观测版本
PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code. While recent advances in large language models have shown promise in automating this process, many approaches focus on one-shot generation or rely on predefined execution-and-repair workflows, without explicitly learning a policy for tool use and revision. This stands in sharp contrast to real-world optimization modeling, which is inherently interactive and proceeds through repeated solve-debug-revise cycles. We introduce PEARL, a system for multi-turn solver-interactive optimization modeling that uses Python execution and mathematical programming solvers inside this loop. Rather than relying on a fixed repair workflow, PEARL learns when to test partial models, how to revise from solver diagnostics, and when to stop. It operates in a multi-turn tool-integrated setting where intermediate execution results, feasibility signals, and solution checks are used to improve both formulations and solver code before finalization. Across diverse optimization benchmarks, PEARL substantially improves executable objective agreement over the same-scale base model and is competitive with strong one-shot and tool-augmented baselines. Notably, PEARL-Qwen3-4B outperforms the much larger DeepSeek-V3.2-685B baseline under direct prompting on both macro- and micro-averaged Pass@1. Code and datasets are provided at https://github.com/AuroraLHL/PEARL.
查看此版本解读 →查看此版本原文 ↗