用统计模型从自然语言生成的解法中选出最优解，大幅提高求解可靠性。
OptiHive: Ensemble Selection for LLM-Based Optimization via Statistical Modeling
- 批量生成多种解法、实例和测试用例，再通过筛选去除错误项。
- 在复杂问题上将最优解率从5%提升至92%。
- 适合需要高可靠性的自动化优化场景，如物流调度。
基于大模型的求解器虽能自动化建模与求解，但稳定性差，常需反复修正导致延迟。我们提出OptiHive框架，可提升任意求解器生成流程的质量，从自然语言描述中生成高质量优化解法。该框架通过一次批量生成多样组件（求解器、问题实例、验证测试），并过滤错误项，确保输出完全可解释。考虑到生成组件的不完美性，引入统计模型推断其真实性能，实现有依据的不确定性量化与求解器选择。在从传统优化问题到多仓库车辆路径问题复杂变体的任务中，OptiHive显著优于基线方法，在最复杂问题上将最优解率从5%提升至92%。
原文摘要 · Abstract (English)
LLM-based solvers have emerged as a promising means of automating problem modeling and solving. However, they remain unreliable and often depend on iterative repair loops that result in significant latency. We introduce OptiHive, a framework that enhances any solver-generation pipeline to produce higher-quality solvers from natural-language descriptions of optimization problems. OptiHive uses a single batched generation to produce diverse components (solvers, problem instances, and validation tests) and filters out erroneous components to ensure fully interpretable outputs. Accounting for the imperfection of the generated components, we employ a statistical model to infer their true performance, enabling principled uncertainty quantification and solver selection. On tasks ranging from traditional optimization problems to challenging variants of the Multi-Depot Vehicle Routing Problem, OptiHive significantly outperforms baselines, increasing the optimality rate from 5% to 92% on the most complex problems.
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