PEARL: New AI System Solves Complex Optimization Problems from Plain Language Instructions
Researchers introduced PEARL, an AI system that translates natural language descriptions of real-world decision problems into formal optimization models and executable solver code. Unlike one-shot approaches, PEARL iteratively refines its solutions by executing code, incorporating solver feedback, and correcting errors — making powerful optimization accessible to non-experts.
Researchers have introduced PEARL, a new AI system that translates everyday language into formal optimization models and executable solver code. Most existing AI tools for this task work in one shot: they produce a formulation once, without executing it, conditioning on solver feedback, or iteratively revising errors. PEARL, however, acts more like a human collaborator — it runs the generated code, analyzes solver output, and refines its approach based on that feedback to find better solutions.
This matters because it makes powerful optimization tools accessible to non-experts. Optimization modeling — the process of translating real-world decision problems into mathematical formulations — is inherently interactive in practice. PEARL's solver-in-the-loop design mirrors this real-world workflow, enabling users to describe problems in natural language and receive progressively improved solutions without needing to learn complex math or programming.
Potential applications include planning a wedding while balancing costs, guest lists, and venue sizes; managing supply chains; allocating resources; or planning projects more efficiently. The system's ability to learn from feedback and correct its own errors represents a significant step toward democratizing optimization for a broader audience.