Search Before You Optimize: Proactive Rule Discovery for Evidence-Grounded Optimization

Constraints required by real-world optimization tasks may originate outside the problem description, and their applicability varies with jurisdiction, time, and task conditions. However, existing methods neither proactively assess whether the available information is sufficient for modeling nor actively search for missing external information, which can lead to modeling failures. To address this gap, we introduce SearchWorthyOR (SWOR), a benchmark built around real-world external rules with three paired conditions: rule inapplicable, applicable but omitted, and applicable and provided. SWOR evaluates rule discovery, applicability assessment, and information integration. We further propose SearchWorthy Agent (SWAgent), which uses an information–evidence table to link conditions requiring verification, supporting evidence, and modeling effects, organize on-demand search, and revise or retain the model according to verified findings. On SWOR, LLM answer accuracy is substantially lower in cases requiring proactive search than in those with rules provided, demonstrating the importance of this challenge for LLM-based automated modeling. SWAgent achieves 79.00% answer accuracy, the highest among the compared workflows, demonstrating its advantage in proactive external search.