PEARL: Interactive Optimization Modeling from Natural Language with Solver-in-the-Loop
Researchers present PEARL, a system that integrates solver feedback and iterative revision into the process of translating natural language descriptions into formal optimization models. PEARL learns when to test partial models and how to revise them based on solver diagnostics, operating in a multi-turn, tool-integrated setting. Experiments show that PEARL achieves higher verified solve rates than both one-shot and tool-augmented baselines, with the 4B-parameter PEARL-Qwen3 model outperforming the much larger 685B-parameter DeepSeek-V3.2 on optimization modeling tasks.
Why it matters: This work demonstrates that interactive, solver-in-the-loop approaches can significantly improve automated optimization modeling, enabling more practical and efficient AI-assisted decision-making.
Full story at: arXiv AI/ML ↗