用模拟预测不确定性，让大模型更可靠地做运筹建模。
Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models
- 通过短时仿真评估中间步骤的未来一致性，识别潜在错误
- 在多个基准上优于标准和低温采样方法，提升建模正确率
- 无需训练即可部署，适合需要高可靠性的工业建模场景
将大语言模型用于运筹学任务仍具挑战性，因正确性依赖连贯的建模过程，而不仅限于最终答案正确。标准自回归生成采用局部贪心策略，常无法预判部分建模步骤是否可扩展为全局一致的优化模型，导致看似合理的中间步骤最终引发严重建模或求解代码错误。为此，我们提出一种无训练、基于不确定性的仿真推理框架，用于运筹学数学建模。该方法不更新模型参数，而是通过短时前瞻仿真评估中间候选步骤的下游预测不确定性或概率集中度，再以重要性重采样动态选择更可能生成一致数学公式的方案。在多个运筹学基准（包括NL4OPT、MAMO和IndustryOR）上的实证评估表明，该框架持续优于标准与低温度基线，建立了一种高效、无需训练的可靠建模生成范式。
原文摘要 · Abstract (English)
Deploying large language models (LLMs) for operations research (OR) tasks remains challenging because correctness depends on a coherent modeling process, not merely a correct final answer. Standard autoregressive generation operates on a myopic policy, which sometimes fails to anticipate whether a partial formulation can be validly extended into a globally consistent optimization model. Consequently, locally plausible steps may propagate into catastrophic downstream formulation or solver code errors. To address this, we propose an uncertainty-aware, training-free inference framework for OR mathematical modeling. Without updating model parameters, our method evaluates intermediate candidate steps using short lookahead simulations to quantify downstream predictive uncertainty or probability concentration. Candidates that demonstrate a higher likelihood of yielding coherent mathematical formulations are then dynamically selected via importance resampling. Empirical evaluations across multiple OR benchmarks (including NL4OPT, MAMO, and IndustryOR) demonstrate that our framework consistently outperforms both standard and low-temperature baselines, establishing an efficient, training-free paradigm for reliable OR formulation generation.
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