预见性学习：供应链中断的智能预测

摘要

在供应链中断实际发生前进行预判，是企业与政策制定者共同面临的核心挑战。关键难点在于如何从嘈杂的非结构化数据中，对低频高影响事件进行可靠推理——这种场景下通用模型若未经任务适配往往表现不佳。我们提出端到端框架，通过以实际中断结果作为监督信号，训练大语言模型生成经过校准的概率预测。该模型在准确度、校准度和精确度上显著优于包括GPT-5在内的强基线模型。研究还表明，训练过程能在无需显式提示的情况下诱导出更结构化、更可靠的概率推理能力。这些成果为训练能产生决策就绪信号的领域专用预测模型提供了通用路径。为促进研究透明性，我们开源了本研究的评估数据集。数据集：https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions

English

Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting where general-purpose models struggle without task-specific adaptation. We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision. The resulting model substantially outperforms strong baselines - including GPT-5 - on accuracy, calibration, and precision. We also show that training induces more structured and reliable probabilistic reasoning without explicit prompting. These results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals. To support transparency we open-source the evaluation dataset used in this study. Dataset: https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions

预见性学习：供应链中断的智能预测

Forecasting Supply Chain Disruptions with Foresight Learning

摘要

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