决策模型 Jev 用于 HVAC 故障诊断：推理需求与分布偏移下的稳健性评估
Where Can a Decision Model Diagnose HVAC Faults? Reasoning Demand, Physical Representation, and Robustness Under Shift
这篇论文拿 Jev 和开源语言模型在 128 个真实故障日上跑九项压力测试，告诉你哪类 HVAC 故障能自动诊断、哪类还得靠人，做楼宇运维的可以看看。
一项研究在来自四个公开真实设备数据集的 128 个故障日上，评估了决策模型 Jev、开源语言模型和监督模型对 HVAC 故障的诊断能力。测试把故障按诊断所需推理强度分级，输入分别给原始数据、物理特征或 Brick 拓扑。结果在九项跨季节、控制配置和建筑的变化测试中，Jev 与较大的开源模型能诊断单一特征可判定的故障，但无法处理需要运行上下文的故障；监督模型在偏移下损失 0.33 macro-F1，而在单一建筑内领先或持平。两类模型的概率仍需校准修正，故障检测能力也偏弱。
Where Can a Decision Model Diagnose HVAC Faults? Reasoning Demand, Physical Representation, and Robustness Under Shift
Artificial intelligence supports building operations in several forms, each with its own barrier. Expert rules must be tuned for every system, supervised models need labeled data that buildings rarely record, and language models return free text that requires human-in-the-loop checking, since their stated confidence is unreliable. A newer kind of pretrained model, here called a decision model, returns a probability for every allowed answer, so one model could serve many decisions without training. This study answers three open questions for fault diagnosis in heating, ventilation, and air-conditioning systems: which decisions such a model can make, what input it needs, and whether its probabilities hold when conditions change. On 128 fault days from four public datasets of real equipment, faults are graded by the reasoning their diagnosis demands, with data given raw, as physical features, or with Brick topology. The decision model Jev, open language models, and a supervised model face nine tests that change season, control configuration, or building. Given physical features, Jev and the larger open model diagnosed faults whose evidence one feature carries, but not faults that need operating context. Under shift they kept their accuracy and calibration, while the supervised model lost 0.33 macro-F1 yet led or tied within a building. Their probabilities still needed correction, and detection was weak. The study maps which faults a decision model can diagnose and from what input, and supports a division of work in which code computes the physics and the model ranks candidate faults for an operator.
