PEARL system uses interactive LLM loop for optimization modeling

Researchers have developed PEARL, an interactive system for optimization modeling that leverages large language models within a solver-in-the-loop framework. Unlike previous one-shot approaches, PEARL iteratively refines mathematical formulations and solver code by incorporating feedback from Python execution and solver diagnostics. This approach significantly enhances the success rate of solving optimization problems, with the PEARL-Qwen3-4B model demonstrating superior performance compared to the larger DeepSeek-V3.2-685B model on optimization modeling tasks. AI

IMPACT Introduces a novel interactive approach for optimization modeling, potentially improving efficiency and accuracy in complex problem-solving.

RANK_REASON The cluster describes a research paper detailing a new system for optimization modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• DeepSeek-V3.2-685B
• Gotit.pub
• Hugging Face
• PEARL
• PEARL-Qwen3-4B
• Python
• ScienceCast

• paper
• model release

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