用表格大模型实现无需训练的多保真回归，提升预测精度与速度。
FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
- 通过低保真模型后验分布引导高保真修正模型，实现跨保真度信息传递。
- 在31个基准任务中，准确率和不确定性量化均优于7种主流方法。
- 无需重训练，适合实时推理场景，尤其适合数据稀疏的工程优化问题。
多保真回归常面临极端数据不平衡问题，传统高斯过程代理模型因计算复杂度为立方级且易在稀疏高保真观测上过拟合，限制了实际应用中的效率与泛化能力。本文提出FIRE，一种无需训练的多保真框架，将表格基础模型（TFM）与高保真修正模型结合，基于低保真模型后验预测分布进行零样本上下文贝叶斯推断。该分布条件下的跨保真信息传递可捕捉异方差误差，实现无需重训练的鲁棒残差学习。在涵盖合成与真实世界任务的31个基准测试中（如DrivAerNet、LCBench），FIRE在性能-时间权衡上优于七种先进高斯过程或深度学习方法，在准确率与不确定性量化方面表现最佳，同时具备运行时优势。局限在于上下文窗口长度限制及对预训练TFM质量的依赖。
原文摘要 · Abstract (English)
Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggle with cubic scaling costs and overfit to sparse high-fidelity observations, limiting efficiency and generalization in real-world applications. We introduce FIRE, a training-free MF framework that couples tabular foundation models (TFMs) to perform zero-shot in-context Bayesian inference via a high-fidelity correction model conditioned on the low-fidelity model's posterior predictive distributions. This cross-fidelity information transfer via distributional summaries captures heteroscedastic errors, enabling robust residual learning without model retraining. Across 31 benchmark problems spanning synthetic and real-world tasks (e.g., DrivAerNet, LCBench), FIRE delivers a stronger performance-time trade-off than seven state-of-the-art GP-based or deep learning MF regression methods, ranking highest in accuracy and uncertainty quantification with runtime advantages. Limitations include context window constraints and dependence on the quality of the pre-trained TFM's.
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