FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models

• URL: http://arxiv.org/abs/2601.22371v1
• Date: Thu, 29 Jan 2026 22:29:58 GMT
• Title: FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
• Authors: Rosen Ting-Ying Yu, Nicholas Sung, Faez Ahmed,
• Abstract summary: Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance.<n>We introduce FIRE, a training-free MF framework.<n>Fire delivers a stronger performance-time trade-off than seven state-of-the-art GP-based or deep learning MF regression methods.
• Score: 3.8824066002669855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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